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Goodman is PI and Director of the BEACON Center for the Study of Evolution in Action, an NSF Science and Technology Center headquartered at Michigan State University, funded at $47.5 million for 2010-20, with a diverse research program and extensive education and outreach programs. BEACON now has a very diverse membership of over 600, including evolutionary biologists and computer scientists/engineers studying evolutionary computation or digital evolution. Goodman received the Ph.D., computer and communication sciences, University of Michigan, 1972. He joined MSU’s faculty in Electrical Engineering and Systems Science in 1971, was promoted to full professor in 1984, and also holds appointments in Mechanical Engineering and in Computer Science and Engineering, in which he has guided many Ph.D. students. He directed the Case Center for Computer-Aided Engineering and Manufacturing from 1983-2002, and founded and directed MSU’s Manufacturing Research Consortium from 1993-2003. He co-founded MSU’s Genetic Algorithms Research and Applications Group (GARAGe) in 1993, conducting many projects under industrial sponsorship. In 1999, he co-founded Red Cedar Technology, Inc., (now a subsidiary of Siemens) which develops design optimization software, and was Vice President for Technology until BEACON was founded in 2010. He was chosen Michigan Distinguished Professor of the Year, 2009, by the Presidents Council, State Universities of Michigan. He was given MSU’s Distinguished Faculty Award in 2011. He was Chair of the Executive Board and a Senior Fellow of the International Society for Genetic and Evolutionary Computation, 2003-2005, and was the founding chair of ACM’s SIG on Genetic and Evolutionary Computation (SIGEVO) in 2005. He also co-leads an information and communication technology for development project in Tanzania.<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="ri-section"><div class="ri-section-header"><span>Interests</span></div><div class="ri-tags-container"><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="32766763" href="https://www.academia.edu/Documents/in/Evolution_of_cooperation_Evolutionary_Biology_"><div id="js-react-on-rails-context" style="display:none" data-rails-context="{"inMailer":false,"i18nLocale":"en","i18nDefaultLocale":"en","href":"https://independent.academia.edu/EricGoodman3","location":"/EricGoodman3","scheme":"https","host":"independent.academia.edu","port":null,"pathname":"/EricGoodman3","search":null,"httpAcceptLanguage":null,"serverSide":false}"></div> <div class="js-react-on-rails-component" style="display:none" 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data-component-name="Pill" data-props="{"color":"gray","children":["Evolutionary Computation"]}" data-trace="false" data-dom-id="Pill-react-component-2c982ae4-335f-4146-ac00-82619c1bacf2"></div> <div id="Pill-react-component-2c982ae4-335f-4146-ac00-82619c1bacf2"></div> </a></div></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Erik Goodman</h3></div><div class="js-work-strip profile--work_container" data-work-id="118154528"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/118154528/Evaluating_Acoustic_Warning_Signals_in_Automobile_Interiors"><img alt="Research paper thumbnail of Evaluating Acoustic Warning Signals in Automobile Interiors" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/118154528/Evaluating_Acoustic_Warning_Signals_in_Automobile_Interiors">Evaluating Acoustic Warning Signals in Automobile Interiors</a></div><div class="wp-workCard_item"><span>SAE Technical Paper Series</span><span>, Feb 1, 1983</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The use of acoustic warning signals to provide information about vehicle conditions to the driver...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The use of acoustic warning signals to provide information about vehicle conditions to the driver and passengers is now possible through application of advanced electronics in recent automobile designs. These acoustic warning signals may be tones or synthesized voice. The signals can only be effective if they are audible and distinguishable by the vehicle occupants without being at an irritating level. This paper presents a method for measuring acoustic intensity in an automobile interior using the cross-spectral technique which may assist in determining audibility of signals. Effective methods for displaying these quantitative vector measurements using computer graphics are presented along with the results of testing. Finally, an important future need of correlating acoustic intensity measurements with published human perception levels is discussed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154528"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154528"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154528; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118154528]").text(description); $(".js-view-count[data-work-id=118154528]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118154528; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118154528']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118154528, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=118154528]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118154528,"title":"Evaluating Acoustic Warning Signals in Automobile Interiors","translated_title":"","metadata":{"abstract":"The use of acoustic warning signals to provide information about vehicle conditions to the driver and passengers is now possible through application of advanced electronics in recent automobile designs. These acoustic warning signals may be tones or synthesized voice. The signals can only be effective if they are audible and distinguishable by the vehicle occupants without being at an irritating level. This paper presents a method for measuring acoustic intensity in an automobile interior using the cross-spectral technique which may assist in determining audibility of signals. Effective methods for displaying these quantitative vector measurements using computer graphics are presented along with the results of testing. Finally, an important future need of correlating acoustic intensity measurements with published human perception levels is discussed.","publication_date":{"day":1,"month":2,"year":1983,"errors":{}},"publication_name":"SAE Technical Paper Series"},"translated_abstract":"The use of acoustic warning signals to provide information about vehicle conditions to the driver and passengers is now possible through application of advanced electronics in recent automobile designs. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154519"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/118154519/Adaptive_walking_control_of_biped_robots_using_online_trajectory_generation_method_based_on_neural_oscillators"><img alt="Research paper thumbnail of Adaptive walking control of biped robots using online trajectory generation method based on neural oscillators" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/118154519/Adaptive_walking_control_of_biped_robots_using_online_trajectory_generation_method_based_on_neural_oscillators">Adaptive walking control of biped robots using online trajectory generation method based on neural oscillators</a></div><div class="wp-workCard_item"><span>Journal of Bionic Engineering</span><span>, Dec 1, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This work concerns biped adaptive walking control on irregular terrains with online trajectory ge...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This work concerns biped adaptive walking control on irregular terrains with online trajectory generation. A new trajectory generation method is proposed based on two neural networks. One oscillatory network is designed to generate foot trajectory, and another set of neural oscillators can generate the trajectory of Center of Mass (CoM) online. Using a motion engine, the characteristics of the workspace are mapped to the joint space. The entraining property of the neural oscillators is exploited for adaptive walking in the absence of a priori knowledge of walking conditions. Sensory feedback is applied to modify the generated trajectories online to improve the walking quality. Furthermore, a staged evolutionary algorithm is developed to tune system parameters to improve walking performance. The developed control strategy is tested using a humanoid robot on irregular terrains. The experiments verify the success of the presented strategy. The biped robot can walk on irregular terrains with varying slopes, unknown bumps and stairs through autonomous adjustment of its walking patterns.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154519"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154519"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154519; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118154519]").text(description); $(".js-view-count[data-work-id=118154519]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118154519; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118154519']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118154519, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=118154519]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118154519,"title":"Adaptive walking control of biped robots using online trajectory generation method based on neural oscillators","translated_title":"","metadata":{"abstract":"This work concerns biped adaptive walking control on irregular terrains with online trajectory generation. 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The biped robot can walk on irregular terrains with varying slopes, unknown bumps and stairs through autonomous adjustment of its walking patterns.","publisher":"Elsevier BV","publication_date":{"day":1,"month":12,"year":2016,"errors":{}},"publication_name":"Journal of Bionic Engineering"},"translated_abstract":"This work concerns biped adaptive walking control on irregular terrains with online trajectory generation. A new trajectory generation method is proposed based on two neural networks. One oscillatory network is designed to generate foot trajectory, and another set of neural oscillators can generate the trajectory of Center of Mass (CoM) online. Using a motion engine, the characteristics of the workspace are mapped to the joint space. The entraining property of the neural oscillators is exploited for adaptive walking in the absence of a priori knowledge of walking conditions. Sensory feedback is applied to modify the generated trajectories online to improve the walking quality. Furthermore, a staged evolutionary algorithm is developed to tune system parameters to improve walking performance. The developed control strategy is tested using a humanoid robot on irregular terrains. The experiments verify the success of the presented strategy. The biped robot can walk on irregular terrains with varying slopes, unknown bumps and stairs through autonomous adjustment of its walking patterns.","internal_url":"https://www.academia.edu/118154519/Adaptive_walking_control_of_biped_robots_using_online_trajectory_generation_method_based_on_neural_oscillators","translated_internal_url":"","created_at":"2024-04-27T05:35:39.441-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32766763,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Adaptive_walking_control_of_biped_robots_using_online_trajectory_generation_method_based_on_neural_oscillators","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1131,"name":"Biomedical Engineering","url":"https://www.academia.edu/Documents/in/Biomedical_Engineering"},{"id":59770,"name":"Trajectory","url":"https://www.academia.edu/Documents/in/Trajectory"},{"id":99861,"name":"ROBOT","url":"https://www.academia.edu/Documents/in/ROBOT"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"}],"urls":[{"id":41439141,"url":"https://doi.org/10.1016/s1672-6529(16)60329-3"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154514"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/118154514/Greenhouse_climate_fuzzy_adaptive_control_considering_energy_saving"><img alt="Research paper thumbnail of Greenhouse climate fuzzy adaptive control considering energy saving" class="work-thumbnail" src="https://attachments.academia-assets.com/113847191/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/118154514/Greenhouse_climate_fuzzy_adaptive_control_considering_energy_saving">Greenhouse climate fuzzy adaptive control considering energy saving</a></div><div class="wp-workCard_item"><span>International Journal of Control Automation and Systems</span><span>, Jun 27, 2017</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="612a1c7d69a7ba45167a08e109fc577a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113847191,"asset_id":118154514,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113847191/download_file?st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154514"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154514"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154514; 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The aim is to ensure the controlled environmental variables to track their desired trajectories so as to create a favorable environment for crop growth. In this method, a feedback linearization technique is first introduced to derive the control laws of heating, fogging and CO 2 injection, then to compensate for the saturation of the actuators, a fuzzy logic system (FLS) is used to approximate the differences between controller outputs and actuator outputs due to actuator constraints. A robust control term is introduced to eliminate the impact of external disturbances and model uncertainty, and finally, Lyapunov stability analysis is performed to guarantee the convergence of the closed-loop system. Taking into account the fact that the crop is usually insensitive to the change of the environment inside the greenhouse during a short time interval, a certain amount of tracking error of the environmental variables is usually acceptable, which means that the environmental variables need only be driven into the corresponding target intervals. In this case, an energy-saving management mechanism is designed to reduce the energy consumption as much as possible. The simulation results illustrate the effectiveness of the proposed control scheme.","publication_date":{"day":27,"month":6,"year":2017,"errors":{}},"publication_name":"International Journal of Control Automation and Systems","grobid_abstract_attachment_id":113847191},"translated_abstract":null,"internal_url":"https://www.academia.edu/118154514/Greenhouse_climate_fuzzy_adaptive_control_considering_energy_saving","translated_internal_url":"","created_at":"2024-04-27T05:35:38.134-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32766763,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":113847191,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113847191/thumbnails/1.jpg","file_name":"s12555-016-0220-620240427-1-khwtxz.pdf","download_url":"https://www.academia.edu/attachments/113847191/download_file?st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Greenhouse_climate_fuzzy_adaptive_contro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113847191/s12555-016-0220-620240427-1-khwtxz-libre.pdf?1714223561=\u0026response-content-disposition=attachment%3B+filename%3DGreenhouse_climate_fuzzy_adaptive_contro.pdf\u0026Expires=1733172574\u0026Signature=BRkw4aGZ3PNDVLMZZonkDYaMtFLMwrwvGEsqO8fmdcm0iPDVk0vA0kkPokU1yYTwRsiwdmmy--~uEFvmPGMJIJdB4PqyHsDJ3FRLgNEu3zqmdLGUgpj2NyMCOmgP~jH5QHt0YHu1LVaEEdUjmkDFkdLLFGsrB77apmQaSfotPS7AvI4DapS9yu0QA~9Xukr6OIeur~hoCR2iLflDcDRvGbUg-ZSQKVkXeDAOog0GMODohdLqIppjQGBPgzLeniikGTKZf6aMedTIDFCjaUzrj4IvZOU0tr7nWAkitp8ihopf86Rjbyc2OxDbe5dUCI2hb17yVEHUgCrl9iazJqcpvw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Greenhouse_climate_fuzzy_adaptive_control_considering_energy_saving","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[{"id":113847191,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113847191/thumbnails/1.jpg","file_name":"s12555-016-0220-620240427-1-khwtxz.pdf","download_url":"https://www.academia.edu/attachments/113847191/download_file?st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Greenhouse_climate_fuzzy_adaptive_contro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113847191/s12555-016-0220-620240427-1-khwtxz-libre.pdf?1714223561=\u0026response-content-disposition=attachment%3B+filename%3DGreenhouse_climate_fuzzy_adaptive_contro.pdf\u0026Expires=1733172574\u0026Signature=BRkw4aGZ3PNDVLMZZonkDYaMtFLMwrwvGEsqO8fmdcm0iPDVk0vA0kkPokU1yYTwRsiwdmmy--~uEFvmPGMJIJdB4PqyHsDJ3FRLgNEu3zqmdLGUgpj2NyMCOmgP~jH5QHt0YHu1LVaEEdUjmkDFkdLLFGsrB77apmQaSfotPS7AvI4DapS9yu0QA~9Xukr6OIeur~hoCR2iLflDcDRvGbUg-ZSQKVkXeDAOog0GMODohdLqIppjQGBPgzLeniikGTKZf6aMedTIDFCjaUzrj4IvZOU0tr7nWAkitp8ihopf86Rjbyc2OxDbe5dUCI2hb17yVEHUgCrl9iazJqcpvw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":3414,"name":"Mechatronics","url":"https://www.academia.edu/Documents/in/Mechatronics"},{"id":4165,"name":"Fuzzy Logic","url":"https://www.academia.edu/Documents/in/Fuzzy_Logic"},{"id":53489,"name":"Automation \u0026 Control Systems","url":"https://www.academia.edu/Documents/in/Automation_and_Control_Systems"},{"id":96825,"name":"Manufacturing Engineering","url":"https://www.academia.edu/Documents/in/Manufacturing_Engineering"},{"id":195712,"name":"GREENHOUSE","url":"https://www.academia.edu/Documents/in/GREENHOUSE"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering"}],"urls":[{"id":41439137,"url":"https://doi.org/10.1007/s12555-016-0220-6"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154510"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/118154510/Solving_metameric_variable_length_optimization_problems_using_genetic_algorithms"><img alt="Research paper thumbnail of Solving metameric variable-length optimization problems using genetic algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/113847193/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/118154510/Solving_metameric_variable_length_optimization_problems_using_genetic_algorithms">Solving metameric variable-length optimization problems using genetic algorithms</a></div><div class="wp-workCard_item"><span>Genetic Programming and Evolvable Machines</span><span>, Sep 26, 2016</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="471865fb58648dceb5eec4c04cccc2d7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113847193,"asset_id":118154510,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113847193/download_file?st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154510"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154510"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154510; 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Examples include the number of sensors in a sensor coverage problem, the number of turbines in a wind farm problem, and the number of plies in a laminate stacking problem. Using standard approaches to solve these problems requires assuming a fixed number of sensors, turbines, or plies. However, if the optimal number is not known a priori this will likely lead to a sub-optimal solution. A better method is to allow the number of components to vary. As the number of components varies, so does the dimensionality of the search space, making the use of gradient-based methods difficult. A metameric genetic algorithm (MGA), which uses a segmented variablelength genome, is proposed. Traditional genetic algorithm (GA) operators, designed to work with fixed-length genomes, are no longer valid. This paper discusses the modifications required for an effective MGA, which is then demonstrated on the aforementioned problems. This includes the representation of the solution in the genome and the recombination, mutation, and selection operators. With these modifications the MGA is able to outperform the fixed-length GA on the selected problems, even if the optimal number of components is assumed to be known a priori.","publication_date":{"day":26,"month":9,"year":2016,"errors":{}},"publication_name":"Genetic Programming and Evolvable Machines","grobid_abstract_attachment_id":113847193},"translated_abstract":null,"internal_url":"https://www.academia.edu/118154510/Solving_metameric_variable_length_optimization_problems_using_genetic_algorithms","translated_internal_url":"","created_at":"2024-04-27T05:35:36.727-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32766763,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":113847193,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113847193/thumbnails/1.jpg","file_name":"s10710-016-9282-820240427-1-eqqkkm.pdf","download_url":"https://www.academia.edu/attachments/113847193/download_file?st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Solving_metameric_variable_length_optimi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113847193/s10710-016-9282-820240427-1-eqqkkm-libre.pdf?1714223574=\u0026response-content-disposition=attachment%3B+filename%3DSolving_metameric_variable_length_optimi.pdf\u0026Expires=1733172574\u0026Signature=YoJzL-P1I7dxZ0pmr0MGdoJMT-9QVksnTw4NOPa0vVxVmNZfmaNMYUDg1~Z0MpbaBoKYZfnEIQzLcjci61SB~nd7DOTCSV-~WiYXucmTgMsKU~ruz7QExBnwvi8TRXVzzOQFE3dfzNI5DWldpS1iplTrq7H9FiO2rMfiJcdv0DaMAGaLOKo9XllqPxRe05TQUMoFsezMao8GLKa~WugR1q9a8SaBUlniIdUUkbZ~iCL8jCwlJ8wTPanxPWnOKsAGn1j~DFPjxvDGowa48-V1GuerHt5u3tmyauHmVl4iRQ3JcgHz-RzjJdO9XHIxdVdqbiemDvRQJxieQX6r2YFjFg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Solving_metameric_variable_length_optimization_problems_using_genetic_algorithms","translated_slug":"","page_count":31,"language":"en","content_type":"Work","owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[{"id":113847193,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113847193/thumbnails/1.jpg","file_name":"s10710-016-9282-820240427-1-eqqkkm.pdf","download_url":"https://www.academia.edu/attachments/113847193/download_file?st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Solving_metameric_variable_length_optimi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113847193/s10710-016-9282-820240427-1-eqqkkm-libre.pdf?1714223574=\u0026response-content-disposition=attachment%3B+filename%3DSolving_metameric_variable_length_optimi.pdf\u0026Expires=1733172574\u0026Signature=YoJzL-P1I7dxZ0pmr0MGdoJMT-9QVksnTw4NOPa0vVxVmNZfmaNMYUDg1~Z0MpbaBoKYZfnEIQzLcjci61SB~nd7DOTCSV-~WiYXucmTgMsKU~ruz7QExBnwvi8TRXVzzOQFE3dfzNI5DWldpS1iplTrq7H9FiO2rMfiJcdv0DaMAGaLOKo9XllqPxRe05TQUMoFsezMao8GLKa~WugR1q9a8SaBUlniIdUUkbZ~iCL8jCwlJ8wTPanxPWnOKsAGn1j~DFPjxvDGowa48-V1GuerHt5u3tmyauHmVl4iRQ3JcgHz-RzjJdO9XHIxdVdqbiemDvRQJxieQX6r2YFjFg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":923,"name":"Technology","url":"https://www.academia.edu/Documents/in/Technology"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm"},{"id":30329,"name":"Genetic Algorithm","url":"https://www.academia.edu/Documents/in/Genetic_Algorithm"},{"id":80414,"name":"Mathematical Sciences","url":"https://www.academia.edu/Documents/in/Mathematical_Sciences"},{"id":272592,"name":"Mathematical Optimization","url":"https://www.academia.edu/Documents/in/Mathematical_Optimization"},{"id":1827413,"name":"Curse of Dimensionality","url":"https://www.academia.edu/Documents/in/Curse_of_Dimensionality"},{"id":3325992,"name":"a priori and a posteriori","url":"https://www.academia.edu/Documents/in/a_priori_and_a_posteriori"}],"urls":[{"id":41439133,"url":"https://doi.org/10.1007/s10710-016-9282-8"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154495"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/118154495/An_improved_MOCC_with_feedback_control_structure_based_on_preference"><img alt="Research paper thumbnail of An improved MOCC with feedback control structure based on preference" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/118154495/An_improved_MOCC_with_feedback_control_structure_based_on_preference">An improved MOCC with feedback control structure based on preference</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT The optimal solution of multi-objective control problem (MOCP) isn&amp;#39;t unique, so ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT The optimal solution of multi-objective control problem (MOCP) isn&amp;#39;t unique, so it is hard for traditional method to obtain these optimal solutions in one simulation process. Based on this background, Multi-Objective Compatible Control (MOCC) algorithm was presented by Lihong Xu in [2]. MOCC is a compromise method, which hunts for suboptimal and feasible region as the control aim rather than precise optimal point. The controller of MOCC is optimized by GA in its interval, namely its controller lacks concrete controller structure. Due to the controller without concrete structure, the system model must be accurate and without input disturbance; however, it is impractical in practice. Besides, the control problem is different from the optimization. Different user has different preference and users&amp;#39; preference information plays a key role in control performance. In this paper, the feedback control law and users&amp;#39; preference information are incorporated into MOCC algorithm. An improved MOCC (IMOCC) algorithm is presented. The simulation result demonstrates its superiority and advantage over the MOCC algorithm.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154495"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154495"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154495; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118154495]").text(description); $(".js-view-count[data-work-id=118154495]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118154495; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118154495']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118154495, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=118154495]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118154495,"title":"An improved MOCC with feedback control structure based on preference","translated_title":"","metadata":{"abstract":"ABSTRACT The optimal solution of multi-objective control problem (MOCP) isn\u0026amp;#39;t unique, so it is hard for traditional method to obtain these optimal solutions in one simulation process. Based on this background, Multi-Objective Compatible Control (MOCC) algorithm was presented by Lihong Xu in [2]. MOCC is a compromise method, which hunts for suboptimal and feasible region as the control aim rather than precise optimal point. The controller of MOCC is optimized by GA in its interval, namely its controller lacks concrete controller structure. Due to the controller without concrete structure, the system model must be accurate and without input disturbance; however, it is impractical in practice. Besides, the control problem is different from the optimization. Different user has different preference and users\u0026amp;#39; preference information plays a key role in control performance. In this paper, the feedback control law and users\u0026amp;#39; preference information are incorporated into MOCC algorithm. An improved MOCC (IMOCC) algorithm is presented. The simulation result demonstrates its superiority and advantage over the MOCC algorithm.","publication_date":{"day":null,"month":null,"year":2009,"errors":{}}},"translated_abstract":"ABSTRACT The optimal solution of multi-objective control problem (MOCP) isn\u0026amp;#39;t unique, so it is hard for traditional method to obtain these optimal solutions in one simulation process. Based on this background, Multi-Objective Compatible Control (MOCC) algorithm was presented by Lihong Xu in [2]. MOCC is a compromise method, which hunts for suboptimal and feasible region as the control aim rather than precise optimal point. The controller of MOCC is optimized by GA in its interval, namely its controller lacks concrete controller structure. Due to the controller without concrete structure, the system model must be accurate and without input disturbance; however, it is impractical in practice. Besides, the control problem is different from the optimization. Different user has different preference and users\u0026amp;#39; preference information plays a key role in control performance. In this paper, the feedback control law and users\u0026amp;#39; preference information are incorporated into MOCC algorithm. An improved MOCC (IMOCC) algorithm is presented. The simulation result demonstrates its superiority and advantage over the MOCC algorithm.","internal_url":"https://www.academia.edu/118154495/An_improved_MOCC_with_feedback_control_structure_based_on_preference","translated_internal_url":"","created_at":"2024-04-27T05:35:32.623-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32766763,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"An_improved_MOCC_with_feedback_control_structure_based_on_preference","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2200,"name":"Optimal Control","url":"https://www.academia.edu/Documents/in/Optimal_Control"},{"id":573872,"name":"Control Management","url":"https://www.academia.edu/Documents/in/Control_Management"},{"id":913924,"name":"Preference","url":"https://www.academia.edu/Documents/in/Preference"}],"urls":[{"id":41439131,"url":"https://doi.org/10.1145/1543834.1543923"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154493"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/118154493/Academic_Biography_of_Erik_D_Goodman"><img alt="Research paper thumbnail of Academic Biography of Erik D. Goodman" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/118154493/Academic_Biography_of_Erik_D_Goodman">Academic Biography of Erik D. Goodman</a></div><div class="wp-workCard_item"><span>Genetic and evolutionary computation</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This contribution summarizes the academic biography of the founding director of the NSF-funded BE...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This contribution summarizes the academic biography of the founding director of the NSF-funded BEACON Center for the Study of Evolution in Action, Dr. Erik D. Goodman, in a first person narrative.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154493"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154493"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154493; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118154493]").text(description); $(".js-view-count[data-work-id=118154493]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118154493; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118154493']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118154493, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=118154493]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118154493,"title":"Academic Biography of Erik D. 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Goodman, in a first person narrative.","internal_url":"https://www.academia.edu/118154493/Academic_Biography_of_Erik_D_Goodman","translated_internal_url":"","created_at":"2024-04-27T05:35:31.704-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32766763,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Academic_Biography_of_Erik_D_Goodman","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[],"research_interests":[{"id":1236,"name":"Art","url":"https://www.academia.edu/Documents/in/Art"},{"id":2852,"name":"Narrative","url":"https://www.academia.edu/Documents/in/Narrative"},{"id":8265,"name":"Biography","url":"https://www.academia.edu/Documents/in/Biography"}],"urls":[{"id":41439122,"url":"https://doi.org/10.1007/978-3-030-39831-6_38"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154490"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/118154490/Investigating_the_Performance_of_Evolutionary_Algorithms_on_Constrained_Multi_objective_Optimization_Problems_with_Deceptive_Infeasible_Regions"><img alt="Research paper thumbnail of Investigating the Performance of Evolutionary Algorithms on Constrained Multi-objective Optimization Problems with Deceptive Infeasible Regions" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/118154490/Investigating_the_Performance_of_Evolutionary_Algorithms_on_Constrained_Multi_objective_Optimization_Problems_with_Deceptive_Infeasible_Regions">Investigating the Performance of Evolutionary Algorithms on Constrained Multi-objective Optimization Problems with Deceptive Infeasible Regions</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">When using constrained multi-objective evolutionary algorithms to solve problems, most of them pr...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">When using constrained multi-objective evolutionary algorithms to solve problems, most of them prefer infeasible solutions closer to feasible regions since they have smaller constraint violations. However, they may not work well on some problems which infeasible solutions closer to feasible regions have larger constraint violations than the ones farther away from the feasible regions. This kind of problems has not been explored so far. To help close this research gap, this paper first illustrates it by designing two new constrained multi-objective optimization problems (CMOPs) with deceptive infeasible regions. That is, in the deceptive infeasible regions, solutions farther away from a feasible region has a smaller constraint violation than the ones closer to the feasible region. We have proposed a constraint-handling technique based a set of directed weights (M2M-DW) to solve CMOPs on our previous work. However, it can not be able to solve the CMOPs with deceptive infeasible regions, since the directed weights prefer infeasible solutions with smaller constraint violations, which guides the search to the less promising regions. We improve it to solve the two proposed problems. Another set of benchmark problems, i.e., CTPs, is also used to verify the performance of the compared algorithms. Experimental results show that the proposed algorithm significantly outperforms the algorithms with which it is compared on the two proposed problems and CTPs, in terms of reliability and stability in finding a set of well-distributed optimal solutions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154490"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154490"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154490; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118154490]").text(description); $(".js-view-count[data-work-id=118154490]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118154490; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118154490']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118154490, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=118154490]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118154490,"title":"Investigating the Performance of Evolutionary Algorithms on Constrained Multi-objective Optimization Problems with Deceptive Infeasible Regions","translated_title":"","metadata":{"abstract":"When using constrained multi-objective evolutionary algorithms to solve problems, most of them prefer infeasible solutions closer to feasible regions since they have smaller constraint violations. However, they may not work well on some problems which infeasible solutions closer to feasible regions have larger constraint violations than the ones farther away from the feasible regions. This kind of problems has not been explored so far. To help close this research gap, this paper first illustrates it by designing two new constrained multi-objective optimization problems (CMOPs) with deceptive infeasible regions. That is, in the deceptive infeasible regions, solutions farther away from a feasible region has a smaller constraint violation than the ones closer to the feasible region. We have proposed a constraint-handling technique based a set of directed weights (M2M-DW) to solve CMOPs on our previous work. However, it can not be able to solve the CMOPs with deceptive infeasible regions, since the directed weights prefer infeasible solutions with smaller constraint violations, which guides the search to the less promising regions. We improve it to solve the two proposed problems. Another set of benchmark problems, i.e., CTPs, is also used to verify the performance of the compared algorithms. Experimental results show that the proposed algorithm significantly outperforms the algorithms with which it is compared on the two proposed problems and CTPs, in terms of reliability and stability in finding a set of well-distributed optimal solutions.","publication_date":{"day":1,"month":12,"year":2019,"errors":{}}},"translated_abstract":"When using constrained multi-objective evolutionary algorithms to solve problems, most of them prefer infeasible solutions closer to feasible regions since they have smaller constraint violations. However, they may not work well on some problems which infeasible solutions closer to feasible regions have larger constraint violations than the ones farther away from the feasible regions. This kind of problems has not been explored so far. To help close this research gap, this paper first illustrates it by designing two new constrained multi-objective optimization problems (CMOPs) with deceptive infeasible regions. That is, in the deceptive infeasible regions, solutions farther away from a feasible region has a smaller constraint violation than the ones closer to the feasible region. We have proposed a constraint-handling technique based a set of directed weights (M2M-DW) to solve CMOPs on our previous work. However, it can not be able to solve the CMOPs with deceptive infeasible regions, since the directed weights prefer infeasible solutions with smaller constraint violations, which guides the search to the less promising regions. We improve it to solve the two proposed problems. Another set of benchmark problems, i.e., CTPs, is also used to verify the performance of the compared algorithms. Experimental results show that the proposed algorithm significantly outperforms the algorithms with which it is compared on the two proposed problems and CTPs, in terms of reliability and stability in finding a set of well-distributed optimal solutions.","internal_url":"https://www.academia.edu/118154490/Investigating_the_Performance_of_Evolutionary_Algorithms_on_Constrained_Multi_objective_Optimization_Problems_with_Deceptive_Infeasible_Regions","translated_internal_url":"","created_at":"2024-04-27T05:35:31.018-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32766763,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Investigating_the_Performance_of_Evolutionary_Algorithms_on_Constrained_Multi_objective_Optimization_Problems_with_Deceptive_Infeasible_Regions","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":272592,"name":"Mathematical Optimization","url":"https://www.academia.edu/Documents/in/Mathematical_Optimization"},{"id":2001284,"name":"Feasible Region","url":"https://www.academia.edu/Documents/in/Feasible_Region"}],"urls":[{"id":41439118,"url":"https://doi.org/10.1109/ssci44817.2019.9002763"}]}, dispatcherData: dispatcherData }); 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To be more specific, the proposed PPS divides the search process into two different stages, including the push and pull search stages. In the push stage, a multiobjective evolutionary algorithm (MOEA) is adopted to explore the search space without considering any constraints, which can help to get across infeasible regions very fast and approach the unconstrained Pareto front. Furthermore, the landscape of CMOPs with constraints can be probed and estimated in the push stage, which can be utilized to conduct the parameters setting for constraint-handling approaches applied in the pull stage. Then, a constrained multi-objective evolutionary algorithm (CMOEA) equipped with an improved epsilon constraint-handling is applied to pull the infeasible individuals achieved in the push stage to the feasible and non-dominated regions. 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THE EFFECT OF WEATHER ON BIOENERGETICS OF BREEDING AMERICAN WOODCOCK&#x27; DALE L. RABE,2...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Page 1. THE EFFECT OF WEATHER ON BIOENERGETICS OF BREEDING AMERICAN WOODCOCK&#x27; DALE L. RABE,2 Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824 HAROLD H. 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While liganddependent steric eects may be involved, the chemistry of the water binding site is also likely to be important. Surprisingly, we show that even without explicit knowledge of the ligand, it is possible to predict most watermediated ligand interactions from the chemical environments of bound water molecules in the free protein structure. A k-nearest-neighbor classier coupled with a genetic algorithm was trained on 13 high-resolution protein structures' rst-shell water molecules, which could be classied as conserved or displaced by comparison with a known ligand-bound structure. When this algorithm was applied to active site water molecules in free protein structures, 70% of the water sites were correctly classied as conserved or displaced upon ligand binding. Discriminant analysis, a traditional statistical technique, was also applied to predict conservation of water sites, and proved to have a similar prediction rate to the genetic algorithm outside active sites, but a random predictive level within active sites. A valuable product of both algorithms is a set of weights for the features used to characterize water-binding sites { hydrogen bonding, neighboring atom hydrophilicity, local mobility, and surface shape { reecting the relative discriminatory ability of each feature in determining whether water is conserved or displaced. Interestingly, the features important for water conservation in active sites dier somewhat from those outside active sites. Together, our results suggest that water-mediated interactions for proteins can be predicted without knowledge of the ligand and that there is a generalizable structural chemistry for water-mediated protein:ligand interactions which can be applied to improve drug design and water 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154451"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/118154451/Synthesis_of_Planar_Mechanisms_Simultaneously_for_Both_Type_and_Dimensionality_Using_Evolutionary_Search_and_Convertible_Agents"><img alt="Research paper thumbnail of Synthesis of Planar Mechanisms Simultaneously for Both Type and Dimensionality Using Evolutionary Search and Convertible Agents" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/118154451/Synthesis_of_Planar_Mechanisms_Simultaneously_for_Both_Type_and_Dimensionality_Using_Evolutionary_Search_and_Convertible_Agents">Synthesis of Planar Mechanisms Simultaneously for Both Type and Dimensionality Using Evolutionary Search and Convertible Agents</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the field of mechanical engineering, synthesizing a mechanism to perform an intended task is d...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the field of mechanical engineering, synthesizing a mechanism to perform an intended task is deceptively complex. In this dissertation, a novel approach to automated mechanism synthesis is described which uses an evolutionary search algorithm and a technique called &quot;convertible agents&quot; to simultaneously find the most appropriate mechanism type for a given problem, while finding an optimum set of dimensions for that mechanism to realize a specified behavior. The convertible agent technique has been developed in response to the unique design challenges encountered when synthesizing a mechanism for both type and dimensionality. Several case studies are presented which demonstrate the approach&#39;s effectiveness over earlier solution strategies. In these studies, six different planar single-degree-of-freedom mechanism types are considered: a four-bar mechanism, Stephenson&#39;s six-bar-mechanisms (types I, II, and III), and Watt&#39;s six-bar-mechanisms (types I and II). The method is readily scalable to account for any number of different mechanism types and complexities. The developed convertible agent approach is well suited for evolutionary design applications outside of mechanism synthesis in which there are a small number of distinct topological design possibilities each with parametric variables to be optimized.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154451"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154451"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154451; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118154451]").text(description); $(".js-view-count[data-work-id=118154451]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118154451; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118154451']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118154451, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=118154451]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118154451,"title":"Synthesis of Planar Mechanisms Simultaneously for Both Type and Dimensionality Using Evolutionary Search and Convertible Agents","translated_title":"","metadata":{"abstract":"In the field of mechanical engineering, synthesizing a mechanism to perform an intended task is deceptively complex. 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The method is readily scalable to account for any number of different mechanism types and complexities. The developed convertible agent approach is well suited for evolutionary design applications outside of mechanism synthesis in which there are a small number of distinct topological design possibilities each with parametric variables to be optimized.","publication_date":{"day":9,"month":9,"year":2011,"errors":{}}},"translated_abstract":"In the field of mechanical engineering, synthesizing a mechanism to perform an intended task is deceptively complex. In this dissertation, a novel approach to automated mechanism synthesis is described which uses an evolutionary search algorithm and a technique called \u0026quot;convertible agents\u0026quot; to simultaneously find the most appropriate mechanism type for a given problem, while finding an optimum set of dimensions for that mechanism to realize a specified behavior. 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These acoustic warning signals may be tones or synthesized voice. The signals can only be effective if they are audible and distinguishable by the vehicle occupants without being at an irritating level. This paper presents a method for measuring acoustic intensity in an automobile interior using the cross-spectral technique which may assist in determining audibility of signals. Effective methods for displaying these quantitative vector measurements using computer graphics are presented along with the results of testing. Finally, an important future need of correlating acoustic intensity measurements with published human perception levels is discussed.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154528"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154528"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154528; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118154528]").text(description); $(".js-view-count[data-work-id=118154528]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118154528; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118154528']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118154528, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=118154528]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118154528,"title":"Evaluating Acoustic Warning Signals in Automobile Interiors","translated_title":"","metadata":{"abstract":"The use of acoustic warning signals to provide information about vehicle conditions to the driver and passengers is now possible through application of advanced electronics in recent automobile designs. These acoustic warning signals may be tones or synthesized voice. The signals can only be effective if they are audible and distinguishable by the vehicle occupants without being at an irritating level. This paper presents a method for measuring acoustic intensity in an automobile interior using the cross-spectral technique which may assist in determining audibility of signals. Effective methods for displaying these quantitative vector measurements using computer graphics are presented along with the results of testing. Finally, an important future need of correlating acoustic intensity measurements with published human perception levels is discussed.","publication_date":{"day":1,"month":2,"year":1983,"errors":{}},"publication_name":"SAE Technical Paper Series"},"translated_abstract":"The use of acoustic warning signals to provide information about vehicle conditions to the driver and passengers is now possible through application of advanced electronics in recent automobile designs. These acoustic warning signals may be tones or synthesized voice. The signals can only be effective if they are audible and distinguishable by the vehicle occupants without being at an irritating level. This paper presents a method for measuring acoustic intensity in an automobile interior using the cross-spectral technique which may assist in determining audibility of signals. Effective methods for displaying these quantitative vector measurements using computer graphics are presented along with the results of testing. Finally, an important future need of correlating acoustic intensity measurements with published human perception levels is discussed.","internal_url":"https://www.academia.edu/118154528/Evaluating_Acoustic_Warning_Signals_in_Automobile_Interiors","translated_internal_url":"","created_at":"2024-04-27T05:35:43.224-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32766763,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Evaluating_Acoustic_Warning_Signals_in_Automobile_Interiors","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":499,"name":"Acoustics","url":"https://www.academia.edu/Documents/in/Acoustics"}],"urls":[{"id":41439149,"url":"https://doi.org/10.4271/830200"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154519"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/118154519/Adaptive_walking_control_of_biped_robots_using_online_trajectory_generation_method_based_on_neural_oscillators"><img alt="Research paper thumbnail of Adaptive walking control of biped robots using online trajectory generation method based on neural oscillators" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/118154519/Adaptive_walking_control_of_biped_robots_using_online_trajectory_generation_method_based_on_neural_oscillators">Adaptive walking control of biped robots using online trajectory generation method based on neural oscillators</a></div><div class="wp-workCard_item"><span>Journal of Bionic Engineering</span><span>, Dec 1, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This work concerns biped adaptive walking control on irregular terrains with online trajectory ge...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This work concerns biped adaptive walking control on irregular terrains with online trajectory generation. A new trajectory generation method is proposed based on two neural networks. One oscillatory network is designed to generate foot trajectory, and another set of neural oscillators can generate the trajectory of Center of Mass (CoM) online. Using a motion engine, the characteristics of the workspace are mapped to the joint space. The entraining property of the neural oscillators is exploited for adaptive walking in the absence of a priori knowledge of walking conditions. Sensory feedback is applied to modify the generated trajectories online to improve the walking quality. Furthermore, a staged evolutionary algorithm is developed to tune system parameters to improve walking performance. The developed control strategy is tested using a humanoid robot on irregular terrains. The experiments verify the success of the presented strategy. The biped robot can walk on irregular terrains with varying slopes, unknown bumps and stairs through autonomous adjustment of its walking patterns.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154519"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154519"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154519; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118154519]").text(description); $(".js-view-count[data-work-id=118154519]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118154519; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118154519']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118154519, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=118154519]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118154519,"title":"Adaptive walking control of biped robots using online trajectory generation method based on neural oscillators","translated_title":"","metadata":{"abstract":"This work concerns biped adaptive walking control on irregular terrains with online trajectory generation. A new trajectory generation method is proposed based on two neural networks. One oscillatory network is designed to generate foot trajectory, and another set of neural oscillators can generate the trajectory of Center of Mass (CoM) online. Using a motion engine, the characteristics of the workspace are mapped to the joint space. The entraining property of the neural oscillators is exploited for adaptive walking in the absence of a priori knowledge of walking conditions. Sensory feedback is applied to modify the generated trajectories online to improve the walking quality. Furthermore, a staged evolutionary algorithm is developed to tune system parameters to improve walking performance. The developed control strategy is tested using a humanoid robot on irregular terrains. The experiments verify the success of the presented strategy. The biped robot can walk on irregular terrains with varying slopes, unknown bumps and stairs through autonomous adjustment of its walking patterns.","publisher":"Elsevier BV","publication_date":{"day":1,"month":12,"year":2016,"errors":{}},"publication_name":"Journal of Bionic Engineering"},"translated_abstract":"This work concerns biped adaptive walking control on irregular terrains with online trajectory generation. A new trajectory generation method is proposed based on two neural networks. One oscillatory network is designed to generate foot trajectory, and another set of neural oscillators can generate the trajectory of Center of Mass (CoM) online. Using a motion engine, the characteristics of the workspace are mapped to the joint space. The entraining property of the neural oscillators is exploited for adaptive walking in the absence of a priori knowledge of walking conditions. Sensory feedback is applied to modify the generated trajectories online to improve the walking quality. Furthermore, a staged evolutionary algorithm is developed to tune system parameters to improve walking performance. The developed control strategy is tested using a humanoid robot on irregular terrains. The experiments verify the success of the presented strategy. The biped robot can walk on irregular terrains with varying slopes, unknown bumps and stairs through autonomous adjustment of its walking patterns.","internal_url":"https://www.academia.edu/118154519/Adaptive_walking_control_of_biped_robots_using_online_trajectory_generation_method_based_on_neural_oscillators","translated_internal_url":"","created_at":"2024-04-27T05:35:39.441-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32766763,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Adaptive_walking_control_of_biped_robots_using_online_trajectory_generation_method_based_on_neural_oscillators","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":1131,"name":"Biomedical Engineering","url":"https://www.academia.edu/Documents/in/Biomedical_Engineering"},{"id":59770,"name":"Trajectory","url":"https://www.academia.edu/Documents/in/Trajectory"},{"id":99861,"name":"ROBOT","url":"https://www.academia.edu/Documents/in/ROBOT"},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"}],"urls":[{"id":41439141,"url":"https://doi.org/10.1016/s1672-6529(16)60329-3"}]}, dispatcherData: dispatcherData }); 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The aim is to ensure the controlled environmental variables to track their desired trajectories so as to create a favorable environment for crop growth. In this method, a feedback linearization technique is first introduced to derive the control laws of heating, fogging and CO 2 injection, then to compensate for the saturation of the actuators, a fuzzy logic system (FLS) is used to approximate the differences between controller outputs and actuator outputs due to actuator constraints. A robust control term is introduced to eliminate the impact of external disturbances and model uncertainty, and finally, Lyapunov stability analysis is performed to guarantee the convergence of the closed-loop system. Taking into account the fact that the crop is usually insensitive to the change of the environment inside the greenhouse during a short time interval, a certain amount of tracking error of the environmental variables is usually acceptable, which means that the environmental variables need only be driven into the corresponding target intervals. In this case, an energy-saving management mechanism is designed to reduce the energy consumption as much as possible. The simulation results illustrate the effectiveness of the proposed control scheme.","publication_date":{"day":27,"month":6,"year":2017,"errors":{}},"publication_name":"International Journal of Control Automation and Systems","grobid_abstract_attachment_id":113847191},"translated_abstract":null,"internal_url":"https://www.academia.edu/118154514/Greenhouse_climate_fuzzy_adaptive_control_considering_energy_saving","translated_internal_url":"","created_at":"2024-04-27T05:35:38.134-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32766763,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":113847191,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113847191/thumbnails/1.jpg","file_name":"s12555-016-0220-620240427-1-khwtxz.pdf","download_url":"https://www.academia.edu/attachments/113847191/download_file?st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Greenhouse_climate_fuzzy_adaptive_contro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113847191/s12555-016-0220-620240427-1-khwtxz-libre.pdf?1714223561=\u0026response-content-disposition=attachment%3B+filename%3DGreenhouse_climate_fuzzy_adaptive_contro.pdf\u0026Expires=1733172574\u0026Signature=BRkw4aGZ3PNDVLMZZonkDYaMtFLMwrwvGEsqO8fmdcm0iPDVk0vA0kkPokU1yYTwRsiwdmmy--~uEFvmPGMJIJdB4PqyHsDJ3FRLgNEu3zqmdLGUgpj2NyMCOmgP~jH5QHt0YHu1LVaEEdUjmkDFkdLLFGsrB77apmQaSfotPS7AvI4DapS9yu0QA~9Xukr6OIeur~hoCR2iLflDcDRvGbUg-ZSQKVkXeDAOog0GMODohdLqIppjQGBPgzLeniikGTKZf6aMedTIDFCjaUzrj4IvZOU0tr7nWAkitp8ihopf86Rjbyc2OxDbe5dUCI2hb17yVEHUgCrl9iazJqcpvw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Greenhouse_climate_fuzzy_adaptive_control_considering_energy_saving","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[{"id":113847191,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113847191/thumbnails/1.jpg","file_name":"s12555-016-0220-620240427-1-khwtxz.pdf","download_url":"https://www.academia.edu/attachments/113847191/download_file?st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Greenhouse_climate_fuzzy_adaptive_contro.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113847191/s12555-016-0220-620240427-1-khwtxz-libre.pdf?1714223561=\u0026response-content-disposition=attachment%3B+filename%3DGreenhouse_climate_fuzzy_adaptive_contro.pdf\u0026Expires=1733172574\u0026Signature=BRkw4aGZ3PNDVLMZZonkDYaMtFLMwrwvGEsqO8fmdcm0iPDVk0vA0kkPokU1yYTwRsiwdmmy--~uEFvmPGMJIJdB4PqyHsDJ3FRLgNEu3zqmdLGUgpj2NyMCOmgP~jH5QHt0YHu1LVaEEdUjmkDFkdLLFGsrB77apmQaSfotPS7AvI4DapS9yu0QA~9Xukr6OIeur~hoCR2iLflDcDRvGbUg-ZSQKVkXeDAOog0GMODohdLqIppjQGBPgzLeniikGTKZf6aMedTIDFCjaUzrj4IvZOU0tr7nWAkitp8ihopf86Rjbyc2OxDbe5dUCI2hb17yVEHUgCrl9iazJqcpvw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":3414,"name":"Mechatronics","url":"https://www.academia.edu/Documents/in/Mechatronics"},{"id":4165,"name":"Fuzzy Logic","url":"https://www.academia.edu/Documents/in/Fuzzy_Logic"},{"id":53489,"name":"Automation \u0026 Control Systems","url":"https://www.academia.edu/Documents/in/Automation_and_Control_Systems"},{"id":96825,"name":"Manufacturing Engineering","url":"https://www.academia.edu/Documents/in/Manufacturing_Engineering"},{"id":195712,"name":"GREENHOUSE","url":"https://www.academia.edu/Documents/in/GREENHOUSE"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering"}],"urls":[{"id":41439137,"url":"https://doi.org/10.1007/s12555-016-0220-6"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154510"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/118154510/Solving_metameric_variable_length_optimization_problems_using_genetic_algorithms"><img alt="Research paper thumbnail of Solving metameric variable-length optimization problems using genetic algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/113847193/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/118154510/Solving_metameric_variable_length_optimization_problems_using_genetic_algorithms">Solving metameric variable-length optimization problems using genetic algorithms</a></div><div class="wp-workCard_item"><span>Genetic Programming and Evolvable Machines</span><span>, Sep 26, 2016</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="471865fb58648dceb5eec4c04cccc2d7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113847193,"asset_id":118154510,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113847193/download_file?st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154510"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154510"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154510; 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Examples include the number of sensors in a sensor coverage problem, the number of turbines in a wind farm problem, and the number of plies in a laminate stacking problem. Using standard approaches to solve these problems requires assuming a fixed number of sensors, turbines, or plies. However, if the optimal number is not known a priori this will likely lead to a sub-optimal solution. A better method is to allow the number of components to vary. As the number of components varies, so does the dimensionality of the search space, making the use of gradient-based methods difficult. A metameric genetic algorithm (MGA), which uses a segmented variablelength genome, is proposed. Traditional genetic algorithm (GA) operators, designed to work with fixed-length genomes, are no longer valid. This paper discusses the modifications required for an effective MGA, which is then demonstrated on the aforementioned problems. This includes the representation of the solution in the genome and the recombination, mutation, and selection operators. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154495"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/118154495/An_improved_MOCC_with_feedback_control_structure_based_on_preference"><img alt="Research paper thumbnail of An improved MOCC with feedback control structure based on preference" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/118154495/An_improved_MOCC_with_feedback_control_structure_based_on_preference">An improved MOCC with feedback control structure based on preference</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT The optimal solution of multi-objective control problem (MOCP) isn&amp;#39;t unique, so ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT The optimal solution of multi-objective control problem (MOCP) isn&amp;#39;t unique, so it is hard for traditional method to obtain these optimal solutions in one simulation process. Based on this background, Multi-Objective Compatible Control (MOCC) algorithm was presented by Lihong Xu in [2]. MOCC is a compromise method, which hunts for suboptimal and feasible region as the control aim rather than precise optimal point. The controller of MOCC is optimized by GA in its interval, namely its controller lacks concrete controller structure. Due to the controller without concrete structure, the system model must be accurate and without input disturbance; however, it is impractical in practice. Besides, the control problem is different from the optimization. Different user has different preference and users&amp;#39; preference information plays a key role in control performance. In this paper, the feedback control law and users&amp;#39; preference information are incorporated into MOCC algorithm. An improved MOCC (IMOCC) algorithm is presented. The simulation result demonstrates its superiority and advantage over the MOCC algorithm.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154495"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154495"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154495; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118154495]").text(description); $(".js-view-count[data-work-id=118154495]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118154495; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118154495']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118154495, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=118154495]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118154495,"title":"An improved MOCC with feedback control structure based on preference","translated_title":"","metadata":{"abstract":"ABSTRACT The optimal solution of multi-objective control problem (MOCP) isn\u0026amp;#39;t unique, so it is hard for traditional method to obtain these optimal solutions in one simulation process. Based on this background, Multi-Objective Compatible Control (MOCC) algorithm was presented by Lihong Xu in [2]. MOCC is a compromise method, which hunts for suboptimal and feasible region as the control aim rather than precise optimal point. The controller of MOCC is optimized by GA in its interval, namely its controller lacks concrete controller structure. Due to the controller without concrete structure, the system model must be accurate and without input disturbance; however, it is impractical in practice. Besides, the control problem is different from the optimization. Different user has different preference and users\u0026amp;#39; preference information plays a key role in control performance. In this paper, the feedback control law and users\u0026amp;#39; preference information are incorporated into MOCC algorithm. An improved MOCC (IMOCC) algorithm is presented. The simulation result demonstrates its superiority and advantage over the MOCC algorithm.","publication_date":{"day":null,"month":null,"year":2009,"errors":{}}},"translated_abstract":"ABSTRACT The optimal solution of multi-objective control problem (MOCP) isn\u0026amp;#39;t unique, so it is hard for traditional method to obtain these optimal solutions in one simulation process. Based on this background, Multi-Objective Compatible Control (MOCC) algorithm was presented by Lihong Xu in [2]. MOCC is a compromise method, which hunts for suboptimal and feasible region as the control aim rather than precise optimal point. The controller of MOCC is optimized by GA in its interval, namely its controller lacks concrete controller structure. Due to the controller without concrete structure, the system model must be accurate and without input disturbance; however, it is impractical in practice. Besides, the control problem is different from the optimization. Different user has different preference and users\u0026amp;#39; preference information plays a key role in control performance. In this paper, the feedback control law and users\u0026amp;#39; preference information are incorporated into MOCC algorithm. An improved MOCC (IMOCC) algorithm is presented. The simulation result demonstrates its superiority and advantage over the MOCC algorithm.","internal_url":"https://www.academia.edu/118154495/An_improved_MOCC_with_feedback_control_structure_based_on_preference","translated_internal_url":"","created_at":"2024-04-27T05:35:32.623-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32766763,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"An_improved_MOCC_with_feedback_control_structure_based_on_preference","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":2200,"name":"Optimal Control","url":"https://www.academia.edu/Documents/in/Optimal_Control"},{"id":573872,"name":"Control Management","url":"https://www.academia.edu/Documents/in/Control_Management"},{"id":913924,"name":"Preference","url":"https://www.academia.edu/Documents/in/Preference"}],"urls":[{"id":41439131,"url":"https://doi.org/10.1145/1543834.1543923"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154493"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/118154493/Academic_Biography_of_Erik_D_Goodman"><img alt="Research paper thumbnail of Academic Biography of Erik D. Goodman" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/118154493/Academic_Biography_of_Erik_D_Goodman">Academic Biography of Erik D. Goodman</a></div><div class="wp-workCard_item"><span>Genetic and evolutionary computation</span><span>, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This contribution summarizes the academic biography of the founding director of the NSF-funded BE...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">This contribution summarizes the academic biography of the founding director of the NSF-funded BEACON Center for the Study of Evolution in Action, Dr. Erik D. Goodman, in a first person narrative.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154493"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154493"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154493; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118154493]").text(description); $(".js-view-count[data-work-id=118154493]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118154493; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118154493']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118154493, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=118154493]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118154493,"title":"Academic Biography of Erik D. Goodman","translated_title":"","metadata":{"abstract":"This contribution summarizes the academic biography of the founding director of the NSF-funded BEACON Center for the Study of Evolution in Action, Dr. Erik D. Goodman, in a first person narrative.","publisher":"Springer Nature","publication_date":{"day":null,"month":null,"year":2020,"errors":{}},"publication_name":"Genetic and evolutionary computation"},"translated_abstract":"This contribution summarizes the academic biography of the founding director of the NSF-funded BEACON Center for the Study of Evolution in Action, Dr. Erik D. Goodman, in a first person narrative.","internal_url":"https://www.academia.edu/118154493/Academic_Biography_of_Erik_D_Goodman","translated_internal_url":"","created_at":"2024-04-27T05:35:31.704-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32766763,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Academic_Biography_of_Erik_D_Goodman","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[],"research_interests":[{"id":1236,"name":"Art","url":"https://www.academia.edu/Documents/in/Art"},{"id":2852,"name":"Narrative","url":"https://www.academia.edu/Documents/in/Narrative"},{"id":8265,"name":"Biography","url":"https://www.academia.edu/Documents/in/Biography"}],"urls":[{"id":41439122,"url":"https://doi.org/10.1007/978-3-030-39831-6_38"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154490"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/118154490/Investigating_the_Performance_of_Evolutionary_Algorithms_on_Constrained_Multi_objective_Optimization_Problems_with_Deceptive_Infeasible_Regions"><img alt="Research paper thumbnail of Investigating the Performance of Evolutionary Algorithms on Constrained Multi-objective Optimization Problems with Deceptive Infeasible Regions" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/118154490/Investigating_the_Performance_of_Evolutionary_Algorithms_on_Constrained_Multi_objective_Optimization_Problems_with_Deceptive_Infeasible_Regions">Investigating the Performance of Evolutionary Algorithms on Constrained Multi-objective Optimization Problems with Deceptive Infeasible Regions</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">When using constrained multi-objective evolutionary algorithms to solve problems, most of them pr...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">When using constrained multi-objective evolutionary algorithms to solve problems, most of them prefer infeasible solutions closer to feasible regions since they have smaller constraint violations. However, they may not work well on some problems which infeasible solutions closer to feasible regions have larger constraint violations than the ones farther away from the feasible regions. This kind of problems has not been explored so far. To help close this research gap, this paper first illustrates it by designing two new constrained multi-objective optimization problems (CMOPs) with deceptive infeasible regions. That is, in the deceptive infeasible regions, solutions farther away from a feasible region has a smaller constraint violation than the ones closer to the feasible region. We have proposed a constraint-handling technique based a set of directed weights (M2M-DW) to solve CMOPs on our previous work. However, it can not be able to solve the CMOPs with deceptive infeasible regions, since the directed weights prefer infeasible solutions with smaller constraint violations, which guides the search to the less promising regions. We improve it to solve the two proposed problems. Another set of benchmark problems, i.e., CTPs, is also used to verify the performance of the compared algorithms. Experimental results show that the proposed algorithm significantly outperforms the algorithms with which it is compared on the two proposed problems and CTPs, in terms of reliability and stability in finding a set of well-distributed optimal solutions.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154490"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154490"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154490; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118154490]").text(description); $(".js-view-count[data-work-id=118154490]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118154490; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118154490']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118154490, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=118154490]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118154490,"title":"Investigating the Performance of Evolutionary Algorithms on Constrained Multi-objective Optimization Problems with Deceptive Infeasible Regions","translated_title":"","metadata":{"abstract":"When using constrained multi-objective evolutionary algorithms to solve problems, most of them prefer infeasible solutions closer to feasible regions since they have smaller constraint violations. However, they may not work well on some problems which infeasible solutions closer to feasible regions have larger constraint violations than the ones farther away from the feasible regions. This kind of problems has not been explored so far. To help close this research gap, this paper first illustrates it by designing two new constrained multi-objective optimization problems (CMOPs) with deceptive infeasible regions. That is, in the deceptive infeasible regions, solutions farther away from a feasible region has a smaller constraint violation than the ones closer to the feasible region. We have proposed a constraint-handling technique based a set of directed weights (M2M-DW) to solve CMOPs on our previous work. However, it can not be able to solve the CMOPs with deceptive infeasible regions, since the directed weights prefer infeasible solutions with smaller constraint violations, which guides the search to the less promising regions. We improve it to solve the two proposed problems. Another set of benchmark problems, i.e., CTPs, is also used to verify the performance of the compared algorithms. Experimental results show that the proposed algorithm significantly outperforms the algorithms with which it is compared on the two proposed problems and CTPs, in terms of reliability and stability in finding a set of well-distributed optimal solutions.","publication_date":{"day":1,"month":12,"year":2019,"errors":{}}},"translated_abstract":"When using constrained multi-objective evolutionary algorithms to solve problems, most of them prefer infeasible solutions closer to feasible regions since they have smaller constraint violations. However, they may not work well on some problems which infeasible solutions closer to feasible regions have larger constraint violations than the ones farther away from the feasible regions. This kind of problems has not been explored so far. To help close this research gap, this paper first illustrates it by designing two new constrained multi-objective optimization problems (CMOPs) with deceptive infeasible regions. That is, in the deceptive infeasible regions, solutions farther away from a feasible region has a smaller constraint violation than the ones closer to the feasible region. We have proposed a constraint-handling technique based a set of directed weights (M2M-DW) to solve CMOPs on our previous work. However, it can not be able to solve the CMOPs with deceptive infeasible regions, since the directed weights prefer infeasible solutions with smaller constraint violations, which guides the search to the less promising regions. We improve it to solve the two proposed problems. Another set of benchmark problems, i.e., CTPs, is also used to verify the performance of the compared algorithms. Experimental results show that the proposed algorithm significantly outperforms the algorithms with which it is compared on the two proposed problems and CTPs, in terms of reliability and stability in finding a set of well-distributed optimal solutions.","internal_url":"https://www.academia.edu/118154490/Investigating_the_Performance_of_Evolutionary_Algorithms_on_Constrained_Multi_objective_Optimization_Problems_with_Deceptive_Infeasible_Regions","translated_internal_url":"","created_at":"2024-04-27T05:35:31.018-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32766763,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Investigating_the_Performance_of_Evolutionary_Algorithms_on_Constrained_Multi_objective_Optimization_Problems_with_Deceptive_Infeasible_Regions","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":272592,"name":"Mathematical Optimization","url":"https://www.academia.edu/Documents/in/Mathematical_Optimization"},{"id":2001284,"name":"Feasible Region","url":"https://www.academia.edu/Documents/in/Feasible_Region"}],"urls":[{"id":41439118,"url":"https://doi.org/10.1109/ssci44817.2019.9002763"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154485"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/118154485/Push_and_Pull_Search_for_Solving_Constrained_Multi_objective_Optimization_Problems"><img alt="Research paper thumbnail of Push and Pull Search for Solving Constrained Multi-objective Optimization Problems" class="work-thumbnail" src="https://attachments.academia-assets.com/113847112/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/118154485/Push_and_Pull_Search_for_Solving_Constrained_Multi_objective_Optimization_Problems">Push and Pull Search for Solving Constrained Multi-objective Optimization Problems</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Sep 15, 2017</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="175e0b22a8568d95502b42a13ea73b86" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113847112,"asset_id":118154485,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113847112/download_file?st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154485"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154485"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154485; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118154485]").text(description); $(".js-view-count[data-work-id=118154485]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118154485; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118154485']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118154485, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "175e0b22a8568d95502b42a13ea73b86" } } $('.js-work-strip[data-work-id=118154485]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118154485,"title":"Push and Pull Search for Solving Constrained Multi-objective Optimization Problems","translated_title":"","metadata":{"publisher":"Cornell University","grobid_abstract":"This paper proposes a push and pull search (PPS) framework for solving constrained multi-objective optimization problems (CMOPs). To be more specific, the proposed PPS divides the search process into two different stages, including the push and pull search stages. In the push stage, a multiobjective evolutionary algorithm (MOEA) is adopted to explore the search space without considering any constraints, which can help to get across infeasible regions very fast and approach the unconstrained Pareto front. Furthermore, the landscape of CMOPs with constraints can be probed and estimated in the push stage, which can be utilized to conduct the parameters setting for constraint-handling approaches applied in the pull stage. Then, a constrained multi-objective evolutionary algorithm (CMOEA) equipped with an improved epsilon constraint-handling is applied to pull the infeasible individuals achieved in the push stage to the feasible and non-dominated regions. Compared with other CMOEAs, the proposed PPS method can more efficiently get across infeasible regions and converge to the feasible and nondominated regions by applying push and pull search strategies at different stages. To evaluate the performance regarding convergence and diversity, a set of benchmark CMOPs is used to test the proposed PPS and compare with other five CMOEAs, including MOEA/D-CDP, MOEA/D-SR, C-MOEA/D, MOEA/D-Epsilon and MOEA/D-IEpsilon. The comprehensive experimental results demonstrate that the proposed PPS achieves significantly better or competitive performance than the other five CMOEAs on most of the benchmark set.","publication_date":{"day":15,"month":9,"year":2017,"errors":{}},"publication_name":"arXiv (Cornell University)","grobid_abstract_attachment_id":113847112},"translated_abstract":null,"internal_url":"https://www.academia.edu/118154485/Push_and_Pull_Search_for_Solving_Constrained_Multi_objective_Optimization_Problems","translated_internal_url":"","created_at":"2024-04-27T05:35:30.216-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32766763,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":113847112,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113847112/thumbnails/1.jpg","file_name":"1709.pdf","download_url":"https://www.academia.edu/attachments/113847112/download_file?st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Push_and_Pull_Search_for_Solving_Constra.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113847112/1709-libre.pdf?1714223829=\u0026response-content-disposition=attachment%3B+filename%3DPush_and_Pull_Search_for_Solving_Constra.pdf\u0026Expires=1733172574\u0026Signature=ELJYWN8I6g~3yf6nEQdZcy4~JFcGDXA2qfXjo1o~X9WGLjAs5y8UvOreTR8Yim6PwdOpQat6vY2~ewjxaROZWD5XVKtQh05D8mNXUdM4q9wZ7jFAS66lk~tHkb4oAlq2p819Jl3EysDxwreKREmuNRtLQCuaeTVkOfI-65WPacRKxouGAKLz4u6hbYxPDQpLwZ5hpjOvhboimVYoMLDnelg~2HKdkJxZhmYDVddzqPwTQFf39MZsfXa-eefbeNT3~kimvqwCLkxPgWD9Cha-qCvg5uTDm8a0AxYJysd8ZE4gADH0Q3cEZUdOZB39TYm6uFvflSVpCl0wBzZU8I94KA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Push_and_Pull_Search_for_Solving_Constrained_Multi_objective_Optimization_Problems","translated_slug":"","page_count":13,"language":"en","content_type":"Work","owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[{"id":113847112,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113847112/thumbnails/1.jpg","file_name":"1709.pdf","download_url":"https://www.academia.edu/attachments/113847112/download_file?st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Push_and_Pull_Search_for_Solving_Constra.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113847112/1709-libre.pdf?1714223829=\u0026response-content-disposition=attachment%3B+filename%3DPush_and_Pull_Search_for_Solving_Constra.pdf\u0026Expires=1733172574\u0026Signature=ELJYWN8I6g~3yf6nEQdZcy4~JFcGDXA2qfXjo1o~X9WGLjAs5y8UvOreTR8Yim6PwdOpQat6vY2~ewjxaROZWD5XVKtQh05D8mNXUdM4q9wZ7jFAS66lk~tHkb4oAlq2p819Jl3EysDxwreKREmuNRtLQCuaeTVkOfI-65WPacRKxouGAKLz4u6hbYxPDQpLwZ5hpjOvhboimVYoMLDnelg~2HKdkJxZhmYDVddzqPwTQFf39MZsfXa-eefbeNT3~kimvqwCLkxPgWD9Cha-qCvg5uTDm8a0AxYJysd8ZE4gADH0Q3cEZUdOZB39TYm6uFvflSVpCl0wBzZU8I94KA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":113847113,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113847113/thumbnails/1.jpg","file_name":"1709.pdf","download_url":"https://www.academia.edu/attachments/113847113/download_file","bulk_download_file_name":"Push_and_Pull_Search_for_Solving_Constra.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113847113/1709-libre.pdf?1714223823=\u0026response-content-disposition=attachment%3B+filename%3DPush_and_Pull_Search_for_Solving_Constra.pdf\u0026Expires=1733172574\u0026Signature=XpCcuK52ZswL5tSj~lPu2Ev7AGJkH~4Y5pqX63TuBDzjhzz9xorebnrlbLBfUkoGc0tB8XF5h~nCiJlHgmAG4WZ5onDNY1~jsHLsyYMNmGPmW5FqgucYsASfEH4pafwgik839JIbcrWh8sqEp6At7-RhfRCgH1E63VMw964~myKqsOW2Nj~4fNOslsi3IChWU69cX2HjGwMujJOhrEjxWvGwmk~uvLvaVmIVhI7dNay1LZZ7Yco6bKmnaDBK-DoCvqR0FNBB~u0AZZWIjvziO0kz1GJMGglTJdCw~-DZBzDYgJttE~issNfbXHD16L2NMZIPJvFs5gGEDxagqi2xhQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":143163,"name":"Multi objective optimization","url":"https://www.academia.edu/Documents/in/Multi_objective_optimization"},{"id":265625,"name":"Evolutionary Algorithm","url":"https://www.academia.edu/Documents/in/Evolutionary_Algorithm"},{"id":272592,"name":"Mathematical Optimization","url":"https://www.academia.edu/Documents/in/Mathematical_Optimization"},{"id":2001284,"name":"Feasible Region","url":"https://www.academia.edu/Documents/in/Feasible_Region"}],"urls":[{"id":41439115,"url":"http://arxiv.org/pdf/1709.05915"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154482"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/118154482/Predicting_and_analyzing_determinants_of_water_mediated_ligand_recognition"><img alt="Research paper thumbnail of Predicting and analyzing determinants of water-mediated ligand recognition" class="work-thumbnail" src="https://attachments.academia-assets.com/113847173/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/118154482/Predicting_and_analyzing_determinants_of_water_mediated_ligand_recognition">Predicting and analyzing determinants of water-mediated ligand recognition</a></div><div class="wp-workCard_item"><span>Acta Crystallographica Section A</span><span>, Aug 8, 1996</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="2cd7e7cf4b419d6becbff76e666473b7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113847173,"asset_id":118154482,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113847173/download_file?st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154482"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154482"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154482; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154478"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/118154478/NSGANetV2_Evolutionary_Multi_Objective_Surrogate_Assisted_Neural_Architecture_Search"><img alt="Research paper thumbnail of NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search" class="work-thumbnail" src="https://attachments.academia-assets.com/113847110/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/118154478/NSGANetV2_Evolutionary_Multi_Objective_Surrogate_Assisted_Neural_Architecture_Search">NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search</a></div><div class="wp-workCard_item"><span>arXiv (Cornell University)</span><span>, Jul 20, 2020</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9bbd7084f851ec64566757d195b28607" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113847110,"asset_id":118154478,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113847110/download_file?st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154478"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154478"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154478; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154474"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/118154474/A_collaboration_based_particle_swarm_optimizer_for_global_optimization_problems"><img alt="Research paper thumbnail of A collaboration-based particle swarm optimizer for global optimization problems" class="work-thumbnail" src="https://attachments.academia-assets.com/113847176/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/118154474/A_collaboration_based_particle_swarm_optimizer_for_global_optimization_problems">A collaboration-based particle swarm optimizer for global optimization problems</a></div><div class="wp-workCard_item"><span>International Journal of Machine Learning and Cybernetics</span><span>, Mar 24, 2018</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="44db303239fd028496040e82daff338d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":113847176,"asset_id":118154474,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/113847176/download_file?st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&st=MTczMzE2ODk3NCw4LjIyMi4yMDguMTQ2&s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154474"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154474"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154474; 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THE EFFECT OF WEATHER ON BIOENERGETICS OF BREEDING AMERICAN WOODCOCK&#x27; DALE L. RABE,2...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Page 1. THE EFFECT OF WEATHER ON BIOENERGETICS OF BREEDING AMERICAN WOODCOCK&#x27; DALE L. RABE,2 Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824 HAROLD H. 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While liganddependent steric eects may be involved, the chemistry of the water binding site is also likely to be important. Surprisingly, we show that even without explicit knowledge of the ligand, it is possible to predict most watermediated ligand interactions from the chemical environments of bound water molecules in the free protein structure. A k-nearest-neighbor classier coupled with a genetic algorithm was trained on 13 high-resolution protein structures' rst-shell water molecules, which could be classied as conserved or displaced by comparison with a known ligand-bound structure. When this algorithm was applied to active site water molecules in free protein structures, 70% of the water sites were correctly classied as conserved or displaced upon ligand binding. Discriminant analysis, a traditional statistical technique, was also applied to predict conservation of water sites, and proved to have a similar prediction rate to the genetic algorithm outside active sites, but a random predictive level within active sites. A valuable product of both algorithms is a set of weights for the features used to characterize water-binding sites { hydrogen bonding, neighboring atom hydrophilicity, local mobility, and surface shape { reecting the relative discriminatory ability of each feature in determining whether water is conserved or displaced. Interestingly, the features important for water conservation in active sites dier somewhat from those outside active sites. Together, our results suggest that water-mediated interactions for proteins can be predicted without knowledge of the ligand and that there is a generalizable structural chemistry for water-mediated protein:ligand interactions which can be applied to improve drug design and water 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="118154451"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/118154451/Synthesis_of_Planar_Mechanisms_Simultaneously_for_Both_Type_and_Dimensionality_Using_Evolutionary_Search_and_Convertible_Agents"><img alt="Research paper thumbnail of Synthesis of Planar Mechanisms Simultaneously for Both Type and Dimensionality Using Evolutionary Search and Convertible Agents" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/118154451/Synthesis_of_Planar_Mechanisms_Simultaneously_for_Both_Type_and_Dimensionality_Using_Evolutionary_Search_and_Convertible_Agents">Synthesis of Planar Mechanisms Simultaneously for Both Type and Dimensionality Using Evolutionary Search and Convertible Agents</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In the field of mechanical engineering, synthesizing a mechanism to perform an intended task is d...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In the field of mechanical engineering, synthesizing a mechanism to perform an intended task is deceptively complex. In this dissertation, a novel approach to automated mechanism synthesis is described which uses an evolutionary search algorithm and a technique called &quot;convertible agents&quot; to simultaneously find the most appropriate mechanism type for a given problem, while finding an optimum set of dimensions for that mechanism to realize a specified behavior. The convertible agent technique has been developed in response to the unique design challenges encountered when synthesizing a mechanism for both type and dimensionality. Several case studies are presented which demonstrate the approach&#39;s effectiveness over earlier solution strategies. In these studies, six different planar single-degree-of-freedom mechanism types are considered: a four-bar mechanism, Stephenson&#39;s six-bar-mechanisms (types I, II, and III), and Watt&#39;s six-bar-mechanisms (types I and II). The method is readily scalable to account for any number of different mechanism types and complexities. The developed convertible agent approach is well suited for evolutionary design applications outside of mechanism synthesis in which there are a small number of distinct topological design possibilities each with parametric variables to be optimized.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="118154451"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="118154451"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 118154451; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=118154451]").text(description); $(".js-view-count[data-work-id=118154451]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 118154451; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='118154451']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 118154451, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=118154451]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":118154451,"title":"Synthesis of Planar Mechanisms Simultaneously for Both Type and Dimensionality Using Evolutionary Search and Convertible Agents","translated_title":"","metadata":{"abstract":"In the field of mechanical engineering, synthesizing a mechanism to perform an intended task is deceptively complex. In this dissertation, a novel approach to automated mechanism synthesis is described which uses an evolutionary search algorithm and a technique called \u0026quot;convertible agents\u0026quot; to simultaneously find the most appropriate mechanism type for a given problem, while finding an optimum set of dimensions for that mechanism to realize a specified behavior. The convertible agent technique has been developed in response to the unique design challenges encountered when synthesizing a mechanism for both type and dimensionality. Several case studies are presented which demonstrate the approach\u0026#39;s effectiveness over earlier solution strategies. In these studies, six different planar single-degree-of-freedom mechanism types are considered: a four-bar mechanism, Stephenson\u0026#39;s six-bar-mechanisms (types I, II, and III), and Watt\u0026#39;s six-bar-mechanisms (types I and II). The method is readily scalable to account for any number of different mechanism types and complexities. The developed convertible agent approach is well suited for evolutionary design applications outside of mechanism synthesis in which there are a small number of distinct topological design possibilities each with parametric variables to be optimized.","publication_date":{"day":9,"month":9,"year":2011,"errors":{}}},"translated_abstract":"In the field of mechanical engineering, synthesizing a mechanism to perform an intended task is deceptively complex. In this dissertation, a novel approach to automated mechanism synthesis is described which uses an evolutionary search algorithm and a technique called \u0026quot;convertible agents\u0026quot; to simultaneously find the most appropriate mechanism type for a given problem, while finding an optimum set of dimensions for that mechanism to realize a specified behavior. 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The developed convertible agent approach is well suited for evolutionary design applications outside of mechanism synthesis in which there are a small number of distinct topological design possibilities each with parametric variables to be optimized.","internal_url":"https://www.academia.edu/118154451/Synthesis_of_Planar_Mechanisms_Simultaneously_for_Both_Type_and_Dimensionality_Using_Evolutionary_Search_and_Convertible_Agents","translated_internal_url":"","created_at":"2024-04-27T05:35:22.434-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":32766763,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[],"slug":"Synthesis_of_Planar_Mechanisms_Simultaneously_for_Both_Type_and_Dimensionality_Using_Evolutionary_Search_and_Convertible_Agents","translated_slug":"","page_count":null,"language":"en","content_type":"Work","owner":{"id":32766763,"first_name":"Erik","middle_initials":null,"last_name":"Goodman","page_name":"EricGoodman3","domain_name":"independent","created_at":"2015-07-03T08:31:31.493-07:00","display_name":"Erik Goodman","url":"https://independent.academia.edu/EricGoodman3"},"attachments":[],"research_interests":[{"id":48,"name":"Engineering","url":"https://www.academia.edu/Documents/in/Engineering"},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":810972,"name":"Mechanism in Biology","url":"https://www.academia.edu/Documents/in/Mechanism_in_Biology"},{"id":1827413,"name":"Curse of Dimensionality","url":"https://www.academia.edu/Documents/in/Curse_of_Dimensionality"}],"urls":[{"id":41439093,"url":"https://dl.acm.org/citation.cfm?id=1970841"}]}, dispatcherData: dispatcherData }); 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