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In 2001 he received his PhD in engineering sciences and in 2004 he received his habilitation in applied systems engineering, both from the Johannes Kepler University of Linz, Austria. Michael Affenzeller is professor at the Upper Austria University of Applied Sciences, Campus Hagenberg, and head of the research group HEAL. From October 2014 to September 2022 he has served as the head of studies for the Master degree program Software Engineering and as vice dean for R<br /><b>Address: </b>Softwarepark 11 <br />A-4232 Hagenberg <br />Austria<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="suggested-academics-container"><div class="suggested-academics--header"><p class="ds2-5-body-md-bold">Related Authors</p></div><ul class="suggested-user-card-list"><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/EnriqueNaredoGarc%C3%ADa"><img class="profile-avatar u-positionAbsolute" alt="Enrique Naredo García" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" src="https://0.academia-photos.com/251608695/105015664/94210613/s200_enrique.naredo_garc_a.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/EnriqueNaredoGarc%C3%ADa">Enrique Naredo García</a></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/MAffenzeller"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://independent.academia.edu/MAffenzeller">Michael Affenzeller</a></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://unisantos.academia.edu/AntoniLaws"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://unisantos.academia.edu/AntoniLaws">Lord Laws</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Universidade Unisantos</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://fh-steyr.academia.edu/MAffenzeller"><img class="profile-avatar u-positionAbsolute" border="0" alt="" src="//a.academia-assets.com/images/s200_no_pic.png" /></a></div><div class="suggested-user-card__user-info"><a class="suggested-user-card__user-info__header ds2-5-body-sm-bold ds2-5-body-link" href="https://fh-steyr.academia.edu/MAffenzeller">M. 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fa-spinner"></i></li></ul></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="nav-container backbone-profile-documents-nav hidden-xs"><ul class="nav-tablist" role="tablist"><li class="nav-chip active" role="presentation"><a data-section-name="" data-toggle="tab" href="#all" role="tab">all</a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Papers" data-toggle="tab" href="#papers" role="tab" title="Papers"><span>257</span> <span class="ds2-5-body-sm-bold">Papers</span></a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Multi-Agent-Systems" data-toggle="tab" href="#multiagentsystems" role="tab" title="Multi Agent Systems"><span>1</span> <span class="ds2-5-body-sm-bold">Multi Agent Systems</span></a></li></ul></div><div class="divider ds-divider-16" style="margin: 0px;"></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 Michael Affenzeller</h3></div><div class="js-work-strip profile--work_container" data-work-id="104525041"><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/104525041/Parsimony_measures_in_multi_objective_genetic_programming_for_symbolic_regression"><img alt="Research paper thumbnail of Parsimony measures in multi-objective genetic programming for symbolic regression" class="work-thumbnail" src="https://attachments.academia-assets.com/104230775/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/104525041/Parsimony_measures_in_multi_objective_genetic_programming_for_symbolic_regression">Parsimony measures in multi-objective genetic programming for symbolic regression</a></div><div class="wp-workCard_item"><span>Proceedings of the Genetic and Evolutionary Computation Conference Companion</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We investigate in this paper the suitability of multi-objective algorithms for Symbolic Regressio...</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">We investigate in this paper the suitability of multi-objective algorithms for Symbolic Regression (SR), where desired properties of parsimony and diversity are explicitly stated as optimization goals. We evaluate different secondary objectives such as length, complexity and diversity on a selection of symbolic regression benchmark problems. Our experiments comparing two multi-objective evolutionary algorithms against standard GP show that multi-objective configurations combining diversity and parsimony objectives provide the best balance of numerical accuracy and model parsimony, allowing practitioners to select suitable models from a diverse set of solutions on the Pareto front. CCS CONCEPTS • Computing methodologies → Search methodologies; • Theory of computation → Random search heuristics; Theory of randomized search heuristics; • Applied computing → Computeraided design;</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3e15a59aae1ea6183384cd8914456a91" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230775,"asset_id":104525041,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230775/download_file?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="104525041"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525041"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525041; 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$(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="104525040"><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/104525040/Simulation_based_set_up_time_optimization_using_Sim_and_Heuristiclab"><img alt="Research paper thumbnail of Simulation-based set-up time optimization using Sim# and Heuristiclab" class="work-thumbnail" src="https://attachments.academia-assets.com/104230761/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/104525040/Simulation_based_set_up_time_optimization_using_Sim_and_Heuristiclab">Simulation-based set-up time optimization using Sim# and Heuristiclab</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Model building is a fundamental task in simulation-based optimization. In this paper we demonstra...</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">Model building is a fundamental task in simulation-based optimization. In this paper we demonstrate the application of Sim# in combination with HeuristicLab to optimize set-up times of arbitrary machinery. On top of Sim#, custom simulation extensions have been implemented and are used to create a simulation model of real world machinery. These extensions enable the design of simulation components that can be reused within different simulation models. This allows to easily create multiple model implementations that reflect different designs of a machine by using a combination of already existing and adapted components. The resulting model is used as evaluation function for the optimization inside HeuristicLab.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="78a17820fb287d57c5018b46d77d1c2e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230761,"asset_id":104525040,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230761/download_file?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="104525040"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525040"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525040; 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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="104525039"><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/104525039/A_new_solution_encoding_for_simulation_based_multi_objective_workforce_qualification_optimization"><img alt="Research paper thumbnail of A new solution encoding for simulation-based multi-objective workforce qualification optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/104230772/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/104525039/A_new_solution_encoding_for_simulation_based_multi_objective_workforce_qualification_optimization">A new solution encoding for simulation-based multi-objective workforce qualification optimization</a></div><div class="wp-workCard_item"><span>THE EUROPEAN MODELING AND SIMULATION SYMPOSIUM</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Solutions for combinatorial problems can be represented by simple encodings, e.g. vectors of bina...</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">Solutions for combinatorial problems can be represented by simple encodings, e.g. vectors of binary or integer values or permutations. For such encodings, various specialized operators have been proposed and implemented. In workforce qualification optimization, qualification matrices can for example be encoded in the form of binary vectors. Though simple, this encoding is rather general and existing operators might not work too well considering the genotype is a binary vector, whereas the phenotype is a qualification matrix. Therefore, a new solution encoding that assigns a number of workers to qualification groups is implemented. By conducting experiments with NSGA-II and the newly developed encoding, we show that having an appropriate mapping between genotype and phenotype, as well as more specialized genetic operators, helps the overall multiobjective search process. Solutions found using the specialized encoding mostly dominate the ones found using a binary vector encoding.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="784fcf678e31597ac119e519e1b7b336" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230772,"asset_id":104525039,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230772/download_file?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="104525039"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525039"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525039; 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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="104525038"><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/104525038/Modelling_a_clustered_generalized_quadratic_assignment_problem"><img alt="Research paper thumbnail of Modelling a clustered generalized quadratic assignment problem" class="work-thumbnail" src="https://attachments.academia-assets.com/104230759/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/104525038/Modelling_a_clustered_generalized_quadratic_assignment_problem">Modelling a clustered generalized quadratic assignment problem</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper is about the modelling of an assignment problem motivated by a real world problem inst...</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 paper is about the modelling of an assignment problem motivated by a real world problem instance. We consider multiple pieces of equipment which need to be assigned to several locations taking into account capacities as well as relations between equipment and distances between locations. Additionally, a clustering of locations is taken into account that groups locations into areas or fields. It is forbidden to assign the same equipment to locations in different fields. The problem arises in many real world applications such as facility layout or location problems. We discuss the complexity of the problem and prove its NP-hardness. Further two linearization approaches are presented as well as computational studies of the original and the linearized models are conducted. Experimental tests are carried out using CPLEX.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="81baafca27fdf04da57ef249674bcc68" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230759,"asset_id":104525038,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230759/download_file?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="104525038"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525038"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525038; 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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="104525037"><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/104525037/Online_Diversity_Control_in_Symbolic_Regression_via_a_Fast_Hash_based_Tree_Similarity_Measure"><img alt="Research paper thumbnail of Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure" class="work-thumbnail" src="https://attachments.academia-assets.com/104230771/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/104525037/Online_Diversity_Control_in_Symbolic_Regression_via_a_Fast_Hash_based_Tree_Similarity_Measure">Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure</a></div><div class="wp-workCard_item"><span>2019 IEEE Congress on Evolutionary Computation (CEC)</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Diversity represents an important aspect of genetic programming, being directly correlated with s...</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">Diversity represents an important aspect of genetic programming, being directly correlated with search performance. When considered at the genotype level, diversity often requires expensive tree distance measures which have a negative impact on the algorithm's runtime performance. In this work we introduce a fast, hash-based tree distance measure to massively speed-up the calculation of population diversity during the algorithmic run. We combine this measure with the standard GA and the NSGA-II genetic algorithms to steer the search towards higher diversity. We validate the approach on a collection of benchmark problems for symbolic regression where our method consistently outperforms the standard GA as well as NSGA-II configurations with different secondary objectives.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bb672f7e911c6da4e908a7d02f9bc531" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230771,"asset_id":104525037,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230771/download_file?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="104525037"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525037"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525037; 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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="104525035"><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/104525035/Generation_of_dispatching_rules_for_job_sequencing_in_single_machine_environments"><img alt="Research paper thumbnail of Generation of dispatching rules for job sequencing in single-machine environments" class="work-thumbnail" src="https://attachments.academia-assets.com/104230757/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/104525035/Generation_of_dispatching_rules_for_job_sequencing_in_single_machine_environments">Generation of dispatching rules for job sequencing in single-machine environments</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A typical way to schedule a set of jobs is to evaluate and optimize different job sequences and t...</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">A typical way to schedule a set of jobs is to evaluate and optimize different job sequences and then process them in the best found order. This global optimization approach can be applied for any set of jobs. Unfortunately, optimizing a subset of these jobs requires a new optimization run for this particular subsequence. In this paper we show the generation of dispatching rules that aid job sequencing in single machine environments using genetic programming and delta features. The rules are applied to sets of jobs and yield priorities depending on certain characteristics. These priorities are then used to create job orders dynamically depending on the last executed job. Once generated for specific scenarios, the rules provide on-the-fly sequence generation capability for queued subsets of jobs. Finally, we compare the performance and robustness of the generated rules against the scheduling approach.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b838e8a6bf41f8c21252ca317ff9fe9e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230757,"asset_id":104525035,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230757/download_file?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="104525035"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525035"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525035; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b838e8a6bf41f8c21252ca317ff9fe9e" } } $('.js-work-strip[data-work-id=104525035]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104525035,"title":"Generation of dispatching rules for job sequencing in single-machine environments","internal_url":"https://www.academia.edu/104525035/Generation_of_dispatching_rules_for_job_sequencing_in_single_machine_environments","owner_id":33734165,"coauthors_can_edit":true,"owner":{"id":33734165,"first_name":"Michael","middle_initials":"","last_name":"Affenzeller","page_name":"MichaelAffenzeller","domain_name":"fh-ooe","created_at":"2015-08-08T14:45:48.110-07:00","display_name":"Michael Affenzeller","url":"https://fh-ooe.academia.edu/MichaelAffenzeller"},"attachments":[{"id":104230757,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104230757/thumbnails/1.jpg","file_name":"EMSS2016_117.pdf","download_url":"https://www.academia.edu/attachments/104230757/download_file","bulk_download_file_name":"Generation_of_dispatching_rules_for_job.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104230757/EMSS2016_117-libre.pdf?1689239784=\u0026response-content-disposition=attachment%3B+filename%3DGeneration_of_dispatching_rules_for_job.pdf\u0026Expires=1740168252\u0026Signature=MYIc3Q1Jq7-kF2XWptUgjWu8~CE1SLhX5iAVuwxiwBelyZ915bjcOv73rwVjYcPrOJhi5VT2g7ukJgKK7khgzNgnfDt-DQxh3~Rmnpm91vx1YkMkDE~ITHkOniDy1JmzZFeUBXoSexqjPvxdxpEcD7nXk4Jf7Re27u-HqOyD~AZnakVbU-kTvc9abFtIVaPGwc~N3MuLCxjwPNqJW5fkG4nVzUsoiQbgYKThPnPbHXgWk5wwe7MRdFgcaWs8r1imfreEPyI36~JvPfu7~1QnwYAFnqDpMUhWcxruzFy~p0Ad~rjQxkxV1g7sRntdlR-z-xkbYnF01hbD2aAbZdb1IA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":104230758,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104230758/thumbnails/1.jpg","file_name":"EMSS2016_117.pdf","download_url":"https://www.academia.edu/attachments/104230758/download_file","bulk_download_file_name":"Generation_of_dispatching_rules_for_job.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104230758/EMSS2016_117-libre.pdf?1689239788=\u0026response-content-disposition=attachment%3B+filename%3DGeneration_of_dispatching_rules_for_job.pdf\u0026Expires=1740168252\u0026Signature=Pufe26Jta6FP40uet86KkB~jLUkzjtx6rtemLw5P-4rZGcyiJzp6P3wOoKQiMtRep8Jx24hFqJnRQEdIGL4tHnvz9zbmNHxOcPXoYA05pYvLm7oLRBNtuDYO-CDdVA9a6cX6jhtCCk8KE17~tg6uT3-WTmvJuvyY8IF14gkfK6lEdS-qYVwKG7GN-mLfCDKVfrgaxw9xwr0G0dZ4ES8W87nJS6rkMvBLctwvqIp3ad4vH~ycWVJzuUu3e1Jbol4WoNWgUnzntmIbi1iV9~kf1ZNOWe5cLS-LLDDtvXiRtpXmo3xLiLdQBDLMtAKXS8P-CRFpxP6w6K4gM2aVbKvcWQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="104525034"><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/104525034/Enhanced_confidence_interpretations_of_GP_based_ensemble_modeling_results"><img alt="Research paper thumbnail of Enhanced confidence interpretations of GP based ensemble modeling results" class="work-thumbnail" src="https://attachments.academia-assets.com/104230755/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/104525034/Enhanced_confidence_interpretations_of_GP_based_ensemble_modeling_results">Enhanced confidence interpretations of GP based ensemble modeling results</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper we describe the integration of ensemble modeling into genetic programming based cla...</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 this paper we describe the integration of ensemble modeling into genetic programming based classification and discuss concepts how to use genetic programming specific features for achieving new confidence indicators that estimate the trustworthiness of predictions. These new concepts are tested on a real world dataset from the field of medical diagnosis for cancer prediction where the trustworthiness of modeling results is of highest importance.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="65182506db3688e6620a6c7fba00f6fb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230755,"asset_id":104525034,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230755/download_file?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="104525034"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525034"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525034; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=104525034]").text(description); $(".js-view-count[data-work-id=104525034]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 104525034; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='104525034']"); 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></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.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: "65182506db3688e6620a6c7fba00f6fb" } } $('.js-work-strip[data-work-id=104525034]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104525034,"title":"Enhanced confidence interpretations of GP based ensemble modeling results","internal_url":"https://www.academia.edu/104525034/Enhanced_confidence_interpretations_of_GP_based_ensemble_modeling_results","owner_id":33734165,"coauthors_can_edit":true,"owner":{"id":33734165,"first_name":"Michael","middle_initials":"","last_name":"Affenzeller","page_name":"MichaelAffenzeller","domain_name":"fh-ooe","created_at":"2015-08-08T14:45:48.110-07:00","display_name":"Michael Affenzeller","url":"https://fh-ooe.academia.edu/MichaelAffenzeller"},"attachments":[{"id":104230755,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104230755/thumbnails/1.jpg","file_name":"EMSS2012_340.pdf","download_url":"https://www.academia.edu/attachments/104230755/download_file","bulk_download_file_name":"Enhanced_confidence_interpretations_of_G.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104230755/EMSS2012_340-libre.pdf?1689239783=\u0026response-content-disposition=attachment%3B+filename%3DEnhanced_confidence_interpretations_of_G.pdf\u0026Expires=1740168252\u0026Signature=VVt3kE0dwyaV0a~A~4RiNeGpO8-OnG2JlqG2jB-2zc6eAZkt2nX71-DY1gMbF6mXuUa8Sk8Crv8kSiD1sgs5oaXMQkIYst5CCaPOK8PJMHWaVZI2VcL7XTTgGlYanRYodDvaLslx7R5dUYBRKeQtRn4znu0FaLBwXQwy5q2MTXIZDQjCJpBXZhTYBTbAb9Y0zx4jsVMDyzfwN~IJVVYsytcsLZ8y-bYFpXvhfegSvNp5m2aopWcFIsAusFvf0WGMGAnC1qc5gIPyJ1tZZI9NzR-KBV4AFwRsBPzE8zUQE4nB1V9M29oFlAvRevT00gUvlJqY6dpA3dsbwqepeGTkZw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":104230756,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104230756/thumbnails/1.jpg","file_name":"EMSS2012_340.pdf","download_url":"https://www.academia.edu/attachments/104230756/download_file","bulk_download_file_name":"Enhanced_confidence_interpretations_of_G.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104230756/EMSS2012_340-libre.pdf?1689239784=\u0026response-content-disposition=attachment%3B+filename%3DEnhanced_confidence_interpretations_of_G.pdf\u0026Expires=1740168252\u0026Signature=QBBqqUeTCtyu44NaZzY8m-2f7-bhSeoyIqihcelh58pvwghhpap9TfoLsvLT5A379i08IBu5xss-9VYISKxIs3Ad9rdmmF8iU~zRz0A8fOce~2Sljrr4Jbzq0GncbPMayWEWtSE-vBHUxIqSPbmyJqApXZFGe5TxHoVH-4LYdnXqeTv7Y5loxhxq5z2RnCKWuwHgBDRF5oL6hFpZr5hOdHVG47yD-xyTnKt5pVx5ANXDbQ8ZuY9ZdKZvgc57R-4kiAv1WnHS6PZ~MmvvqZXRyiBiHVwvQc~G5e7lAQtnujrQspGPbLmRVowPQQfGOtfLi~0kGzOESu7Ce0WEP4wDfA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="104525033"><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/104525033/On_the_analysis_classification_and_prediction_of_metaheuristic_algorithm_behavior_for_combinatorial_optimization_problems"><img alt="Research paper thumbnail of On the analysis, classification and prediction of metaheuristic algorithm behavior for combinatorial optimization problems" class="work-thumbnail" src="https://attachments.academia-assets.com/104230754/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/104525033/On_the_analysis_classification_and_prediction_of_metaheuristic_algorithm_behavior_for_combinatorial_optimization_problems">On the analysis, classification and prediction of metaheuristic algorithm behavior for combinatorial optimization problems</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Metaheuristics are successfully applied in many different application domains as they provide a r...</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">Metaheuristics are successfully applied in many different application domains as they provide a reasonable tradeoff between computation time and achievable solution quality. However, choosing an appropriate algorithm for a certain problem is not trivial, as problem characteristics can change remarkably for different instances and the performance of a metaheuristic may vary considerably for different parameter settings. Therefore it always takes qualified algorithm experts to select and tune a metaheuristic algorithm for a specific application. This process of algorithm selection and parameter tuning is frequently done manually and intuitively and requires a large number of empirical tests. In this contribution the authors propose several measurement values to characterize the search behavior of different metaheuristics for solving combinatorial optimization problems. Based on these measurements algorithms can be classified and models can be learnt to predict the algorithms behavior ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0a9e4d50bf77797fee24398791ff798f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230754,"asset_id":104525033,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230754/download_file?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="104525033"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525033"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525033; 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Thus it is crucial to model and optimize practical transportation problems. In this paper we present a flexible modeling and optimization framework integrated in the open source environment HeuristicLab. We show, how rich and dynamic vehicle routing problem variants can be integrated in our framework. Using this model, we perform an algorithmic study where we compare several heuristic and metaheuristic algorithms for the dynamic pickup and delivery problem with time windows.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0e49d13bb1d0bdb9603a5b775bf2a00e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230753,"asset_id":104525032,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230753/download_file?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="104525032"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525032"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525032; 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$(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="104525031"><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/104525031/Incorporating_physical_knowledge_about_the_formation_of_nitric_oxides_into_evolutionary_system_identification"><img alt="Research paper thumbnail of Incorporating physical knowledge about the formation of nitric oxides into evolutionary system identification" class="work-thumbnail" src="https://attachments.academia-assets.com/104230751/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/104525031/Incorporating_physical_knowledge_about_the_formation_of_nitric_oxides_into_evolutionary_system_identification">Incorporating physical knowledge about the formation of nitric oxides into evolutionary system identification</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Genetic programming (GP) is an evolutionary optimization method that has already been used succes...</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">Genetic programming (GP) is an evolutionary optimization method that has already been used successfully for solving data mining problems in the context of several scientific domains. For example, the identification of models describing the nitric oxides (NOx) emissions of diesel engines has been investigated intensively, very promising results were obtained using GP. In the standard GP process, all model structures (as well as parameter settings) of models are created during an evolutionary process; populations of models are evolved using the genetic operators crossover, mutation and selection. In this paper we discuss several possibilities how a priori knowledge can be integrated into the GP process; we have used physical knowledge about the formation of NOx emissions in a BMW diesel engine, test results are given in the empirical tests section.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="921735fab8ff90cb535f95cd81319d30" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230751,"asset_id":104525031,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230751/download_file?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="104525031"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525031"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525031; 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</script> <div class="js-work-strip profile--work_container" data-work-id="104525023"><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/104525023/The_Use_of_Genetic_Programming_in_Mechatronics"><img alt="Research paper thumbnail of The Use of Genetic Programming in Mechatronics" 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/104525023/The_Use_of_Genetic_Programming_in_Mechatronics">The Use of Genetic Programming in Mechatronics</a></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="104525023"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525023"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525023; 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A separation of the modeling process from the implementation of the algorithmic concepts improves the communication and collaboration of practitioners, optimization experts and programmers. This is achieved by providing a higher level of abstraction compared to a general-purpose programming language. A generic and extensible modeling concept is presented and several example algorithm models are illustrated.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d49b1219cbc207aba65635fdf1a91119" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230769,"asset_id":104525022,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230769/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="104525022"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525022"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525022; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="3353306" id="papers"><div class="js-work-strip profile--work_container" data-work-id="104525041"><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/104525041/Parsimony_measures_in_multi_objective_genetic_programming_for_symbolic_regression"><img alt="Research paper thumbnail of Parsimony measures in multi-objective genetic programming for symbolic regression" class="work-thumbnail" src="https://attachments.academia-assets.com/104230775/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/104525041/Parsimony_measures_in_multi_objective_genetic_programming_for_symbolic_regression">Parsimony measures in multi-objective genetic programming for symbolic regression</a></div><div class="wp-workCard_item"><span>Proceedings of the Genetic and Evolutionary Computation Conference Companion</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We investigate in this paper the suitability of multi-objective algorithms for Symbolic Regressio...</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">We investigate in this paper the suitability of multi-objective algorithms for Symbolic Regression (SR), where desired properties of parsimony and diversity are explicitly stated as optimization goals. We evaluate different secondary objectives such as length, complexity and diversity on a selection of symbolic regression benchmark problems. Our experiments comparing two multi-objective evolutionary algorithms against standard GP show that multi-objective configurations combining diversity and parsimony objectives provide the best balance of numerical accuracy and model parsimony, allowing practitioners to select suitable models from a diverse set of solutions on the Pareto front. CCS CONCEPTS • Computing methodologies → Search methodologies; • Theory of computation → Random search heuristics; Theory of randomized search heuristics; • Applied computing → Computeraided design;</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3e15a59aae1ea6183384cd8914456a91" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230775,"asset_id":104525041,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230775/download_file?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="104525041"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525041"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525041; 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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="104525040"><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/104525040/Simulation_based_set_up_time_optimization_using_Sim_and_Heuristiclab"><img alt="Research paper thumbnail of Simulation-based set-up time optimization using Sim# and Heuristiclab" class="work-thumbnail" src="https://attachments.academia-assets.com/104230761/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/104525040/Simulation_based_set_up_time_optimization_using_Sim_and_Heuristiclab">Simulation-based set-up time optimization using Sim# and Heuristiclab</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Model building is a fundamental task in simulation-based optimization. In this paper we demonstra...</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">Model building is a fundamental task in simulation-based optimization. In this paper we demonstrate the application of Sim# in combination with HeuristicLab to optimize set-up times of arbitrary machinery. On top of Sim#, custom simulation extensions have been implemented and are used to create a simulation model of real world machinery. These extensions enable the design of simulation components that can be reused within different simulation models. This allows to easily create multiple model implementations that reflect different designs of a machine by using a combination of already existing and adapted components. The resulting model is used as evaluation function for the optimization inside HeuristicLab.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="78a17820fb287d57c5018b46d77d1c2e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230761,"asset_id":104525040,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230761/download_file?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="104525040"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525040"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525040; 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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="104525039"><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/104525039/A_new_solution_encoding_for_simulation_based_multi_objective_workforce_qualification_optimization"><img alt="Research paper thumbnail of A new solution encoding for simulation-based multi-objective workforce qualification optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/104230772/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/104525039/A_new_solution_encoding_for_simulation_based_multi_objective_workforce_qualification_optimization">A new solution encoding for simulation-based multi-objective workforce qualification optimization</a></div><div class="wp-workCard_item"><span>THE EUROPEAN MODELING AND SIMULATION SYMPOSIUM</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Solutions for combinatorial problems can be represented by simple encodings, e.g. vectors of bina...</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">Solutions for combinatorial problems can be represented by simple encodings, e.g. vectors of binary or integer values or permutations. For such encodings, various specialized operators have been proposed and implemented. In workforce qualification optimization, qualification matrices can for example be encoded in the form of binary vectors. Though simple, this encoding is rather general and existing operators might not work too well considering the genotype is a binary vector, whereas the phenotype is a qualification matrix. Therefore, a new solution encoding that assigns a number of workers to qualification groups is implemented. By conducting experiments with NSGA-II and the newly developed encoding, we show that having an appropriate mapping between genotype and phenotype, as well as more specialized genetic operators, helps the overall multiobjective search process. Solutions found using the specialized encoding mostly dominate the ones found using a binary vector encoding.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="784fcf678e31597ac119e519e1b7b336" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230772,"asset_id":104525039,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230772/download_file?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="104525039"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525039"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525039; 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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="104525038"><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/104525038/Modelling_a_clustered_generalized_quadratic_assignment_problem"><img alt="Research paper thumbnail of Modelling a clustered generalized quadratic assignment problem" class="work-thumbnail" src="https://attachments.academia-assets.com/104230759/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/104525038/Modelling_a_clustered_generalized_quadratic_assignment_problem">Modelling a clustered generalized quadratic assignment problem</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper is about the modelling of an assignment problem motivated by a real world problem inst...</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 paper is about the modelling of an assignment problem motivated by a real world problem instance. We consider multiple pieces of equipment which need to be assigned to several locations taking into account capacities as well as relations between equipment and distances between locations. Additionally, a clustering of locations is taken into account that groups locations into areas or fields. It is forbidden to assign the same equipment to locations in different fields. The problem arises in many real world applications such as facility layout or location problems. We discuss the complexity of the problem and prove its NP-hardness. Further two linearization approaches are presented as well as computational studies of the original and the linearized models are conducted. Experimental tests are carried out using CPLEX.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="81baafca27fdf04da57ef249674bcc68" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230759,"asset_id":104525038,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230759/download_file?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="104525038"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525038"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525038; 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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="104525037"><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/104525037/Online_Diversity_Control_in_Symbolic_Regression_via_a_Fast_Hash_based_Tree_Similarity_Measure"><img alt="Research paper thumbnail of Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure" class="work-thumbnail" src="https://attachments.academia-assets.com/104230771/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/104525037/Online_Diversity_Control_in_Symbolic_Regression_via_a_Fast_Hash_based_Tree_Similarity_Measure">Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure</a></div><div class="wp-workCard_item"><span>2019 IEEE Congress on Evolutionary Computation (CEC)</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Diversity represents an important aspect of genetic programming, being directly correlated with s...</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">Diversity represents an important aspect of genetic programming, being directly correlated with search performance. When considered at the genotype level, diversity often requires expensive tree distance measures which have a negative impact on the algorithm's runtime performance. In this work we introduce a fast, hash-based tree distance measure to massively speed-up the calculation of population diversity during the algorithmic run. We combine this measure with the standard GA and the NSGA-II genetic algorithms to steer the search towards higher diversity. We validate the approach on a collection of benchmark problems for symbolic regression where our method consistently outperforms the standard GA as well as NSGA-II configurations with different secondary objectives.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="bb672f7e911c6da4e908a7d02f9bc531" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230771,"asset_id":104525037,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230771/download_file?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="104525037"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525037"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525037; 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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="104525035"><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/104525035/Generation_of_dispatching_rules_for_job_sequencing_in_single_machine_environments"><img alt="Research paper thumbnail of Generation of dispatching rules for job sequencing in single-machine environments" class="work-thumbnail" src="https://attachments.academia-assets.com/104230757/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/104525035/Generation_of_dispatching_rules_for_job_sequencing_in_single_machine_environments">Generation of dispatching rules for job sequencing in single-machine environments</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">A typical way to schedule a set of jobs is to evaluate and optimize different job sequences and t...</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">A typical way to schedule a set of jobs is to evaluate and optimize different job sequences and then process them in the best found order. This global optimization approach can be applied for any set of jobs. Unfortunately, optimizing a subset of these jobs requires a new optimization run for this particular subsequence. In this paper we show the generation of dispatching rules that aid job sequencing in single machine environments using genetic programming and delta features. The rules are applied to sets of jobs and yield priorities depending on certain characteristics. These priorities are then used to create job orders dynamically depending on the last executed job. Once generated for specific scenarios, the rules provide on-the-fly sequence generation capability for queued subsets of jobs. Finally, we compare the performance and robustness of the generated rules against the scheduling approach.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b838e8a6bf41f8c21252ca317ff9fe9e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230757,"asset_id":104525035,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230757/download_file?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="104525035"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525035"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525035; 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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="104525034"><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/104525034/Enhanced_confidence_interpretations_of_GP_based_ensemble_modeling_results"><img alt="Research paper thumbnail of Enhanced confidence interpretations of GP based ensemble modeling results" class="work-thumbnail" src="https://attachments.academia-assets.com/104230755/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/104525034/Enhanced_confidence_interpretations_of_GP_based_ensemble_modeling_results">Enhanced confidence interpretations of GP based ensemble modeling results</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In this paper we describe the integration of ensemble modeling into genetic programming based cla...</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 this paper we describe the integration of ensemble modeling into genetic programming based classification and discuss concepts how to use genetic programming specific features for achieving new confidence indicators that estimate the trustworthiness of predictions. These new concepts are tested on a real world dataset from the field of medical diagnosis for cancer prediction where the trustworthiness of modeling results is of highest importance.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="65182506db3688e6620a6c7fba00f6fb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230755,"asset_id":104525034,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230755/download_file?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="104525034"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525034"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525034; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "65182506db3688e6620a6c7fba00f6fb" } } $('.js-work-strip[data-work-id=104525034]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104525034,"title":"Enhanced confidence interpretations of GP based ensemble modeling results","internal_url":"https://www.academia.edu/104525034/Enhanced_confidence_interpretations_of_GP_based_ensemble_modeling_results","owner_id":33734165,"coauthors_can_edit":true,"owner":{"id":33734165,"first_name":"Michael","middle_initials":"","last_name":"Affenzeller","page_name":"MichaelAffenzeller","domain_name":"fh-ooe","created_at":"2015-08-08T14:45:48.110-07:00","display_name":"Michael Affenzeller","url":"https://fh-ooe.academia.edu/MichaelAffenzeller"},"attachments":[{"id":104230755,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104230755/thumbnails/1.jpg","file_name":"EMSS2012_340.pdf","download_url":"https://www.academia.edu/attachments/104230755/download_file","bulk_download_file_name":"Enhanced_confidence_interpretations_of_G.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104230755/EMSS2012_340-libre.pdf?1689239783=\u0026response-content-disposition=attachment%3B+filename%3DEnhanced_confidence_interpretations_of_G.pdf\u0026Expires=1740168252\u0026Signature=VVt3kE0dwyaV0a~A~4RiNeGpO8-OnG2JlqG2jB-2zc6eAZkt2nX71-DY1gMbF6mXuUa8Sk8Crv8kSiD1sgs5oaXMQkIYst5CCaPOK8PJMHWaVZI2VcL7XTTgGlYanRYodDvaLslx7R5dUYBRKeQtRn4znu0FaLBwXQwy5q2MTXIZDQjCJpBXZhTYBTbAb9Y0zx4jsVMDyzfwN~IJVVYsytcsLZ8y-bYFpXvhfegSvNp5m2aopWcFIsAusFvf0WGMGAnC1qc5gIPyJ1tZZI9NzR-KBV4AFwRsBPzE8zUQE4nB1V9M29oFlAvRevT00gUvlJqY6dpA3dsbwqepeGTkZw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":104230756,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104230756/thumbnails/1.jpg","file_name":"EMSS2012_340.pdf","download_url":"https://www.academia.edu/attachments/104230756/download_file","bulk_download_file_name":"Enhanced_confidence_interpretations_of_G.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104230756/EMSS2012_340-libre.pdf?1689239784=\u0026response-content-disposition=attachment%3B+filename%3DEnhanced_confidence_interpretations_of_G.pdf\u0026Expires=1740168252\u0026Signature=QBBqqUeTCtyu44NaZzY8m-2f7-bhSeoyIqihcelh58pvwghhpap9TfoLsvLT5A379i08IBu5xss-9VYISKxIs3Ad9rdmmF8iU~zRz0A8fOce~2Sljrr4Jbzq0GncbPMayWEWtSE-vBHUxIqSPbmyJqApXZFGe5TxHoVH-4LYdnXqeTv7Y5loxhxq5z2RnCKWuwHgBDRF5oL6hFpZr5hOdHVG47yD-xyTnKt5pVx5ANXDbQ8ZuY9ZdKZvgc57R-4kiAv1WnHS6PZ~MmvvqZXRyiBiHVwvQc~G5e7lAQtnujrQspGPbLmRVowPQQfGOtfLi~0kGzOESu7Ce0WEP4wDfA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, 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="104525033"><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/104525033/On_the_analysis_classification_and_prediction_of_metaheuristic_algorithm_behavior_for_combinatorial_optimization_problems"><img alt="Research paper thumbnail of On the analysis, classification and prediction of metaheuristic algorithm behavior for combinatorial optimization problems" class="work-thumbnail" src="https://attachments.academia-assets.com/104230754/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/104525033/On_the_analysis_classification_and_prediction_of_metaheuristic_algorithm_behavior_for_combinatorial_optimization_problems">On the analysis, classification and prediction of metaheuristic algorithm behavior for combinatorial optimization problems</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Metaheuristics are successfully applied in many different application domains as they provide a r...</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">Metaheuristics are successfully applied in many different application domains as they provide a reasonable tradeoff between computation time and achievable solution quality. However, choosing an appropriate algorithm for a certain problem is not trivial, as problem characteristics can change remarkably for different instances and the performance of a metaheuristic may vary considerably for different parameter settings. Therefore it always takes qualified algorithm experts to select and tune a metaheuristic algorithm for a specific application. This process of algorithm selection and parameter tuning is frequently done manually and intuitively and requires a large number of empirical tests. In this contribution the authors propose several measurement values to characterize the search behavior of different metaheuristics for solving combinatorial optimization problems. Based on these measurements algorithms can be classified and models can be learnt to predict the algorithms behavior ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0a9e4d50bf77797fee24398791ff798f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230754,"asset_id":104525033,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230754/download_file?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="104525033"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525033"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525033; 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Thus it is crucial to model and optimize practical transportation problems. In this paper we present a flexible modeling and optimization framework integrated in the open source environment HeuristicLab. We show, how rich and dynamic vehicle routing problem variants can be integrated in our framework. Using this model, we perform an algorithmic study where we compare several heuristic and metaheuristic algorithms for the dynamic pickup and delivery problem with time windows.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0e49d13bb1d0bdb9603a5b775bf2a00e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230753,"asset_id":104525032,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230753/download_file?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="104525032"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525032"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525032; 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$(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="104525031"><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/104525031/Incorporating_physical_knowledge_about_the_formation_of_nitric_oxides_into_evolutionary_system_identification"><img alt="Research paper thumbnail of Incorporating physical knowledge about the formation of nitric oxides into evolutionary system identification" class="work-thumbnail" src="https://attachments.academia-assets.com/104230751/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/104525031/Incorporating_physical_knowledge_about_the_formation_of_nitric_oxides_into_evolutionary_system_identification">Incorporating physical knowledge about the formation of nitric oxides into evolutionary system identification</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Genetic programming (GP) is an evolutionary optimization method that has already been used succes...</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">Genetic programming (GP) is an evolutionary optimization method that has already been used successfully for solving data mining problems in the context of several scientific domains. For example, the identification of models describing the nitric oxides (NOx) emissions of diesel engines has been investigated intensively, very promising results were obtained using GP. In the standard GP process, all model structures (as well as parameter settings) of models are created during an evolutionary process; populations of models are evolved using the genetic operators crossover, mutation and selection. In this paper we discuss several possibilities how a priori knowledge can be integrated into the GP process; we have used physical knowledge about the formation of NOx emissions in a BMW diesel engine, test results are given in the empirical tests section.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="921735fab8ff90cb535f95cd81319d30" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230751,"asset_id":104525031,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230751/download_file?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="104525031"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525031"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525031; 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</script> <div class="js-work-strip profile--work_container" data-work-id="104525023"><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/104525023/The_Use_of_Genetic_Programming_in_Mechatronics"><img alt="Research paper thumbnail of The Use of Genetic Programming in Mechatronics" 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/104525023/The_Use_of_Genetic_Programming_in_Mechatronics">The Use of Genetic Programming in Mechatronics</a></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="104525023"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525023"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525023; 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dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=104525023]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":104525023,"title":"The Use of Genetic Programming in Mechatronics","internal_url":"https://www.academia.edu/104525023/The_Use_of_Genetic_Programming_in_Mechatronics","owner_id":33734165,"coauthors_can_edit":true,"owner":{"id":33734165,"first_name":"Michael","middle_initials":"","last_name":"Affenzeller","page_name":"MichaelAffenzeller","domain_name":"fh-ooe","created_at":"2015-08-08T14:45:48.110-07:00","display_name":"Michael Affenzeller","url":"https://fh-ooe.academia.edu/MichaelAffenzeller"},"attachments":[]}, 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="104525022"><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/104525022/On_the_Benefits_of_a_Domain_Specific_Language_for_Modeling_Metaheuristic_Optimization_Algorithms"><img alt="Research paper thumbnail of On the Benefits of a Domain-Specific Language for Modeling Metaheuristic Optimization Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/104230769/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/104525022/On_the_Benefits_of_a_Domain_Specific_Language_for_Modeling_Metaheuristic_Optimization_Algorithms">On the Benefits of a Domain-Specific Language for Modeling Metaheuristic Optimization Algorithms</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This work provides a case-study of how metaheuristic optimization algorithms can be developed usi...</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 provides a case-study of how metaheuristic optimization algorithms can be developed using a domain-specific language as a separate modeling layer. A separation of the modeling process from the implementation of the algorithmic concepts improves the communication and collaboration of practitioners, optimization experts and programmers. This is achieved by providing a higher level of abstraction compared to a general-purpose programming language. A generic and extensible modeling concept is presented and several example algorithm models are illustrated.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d49b1219cbc207aba65635fdf1a91119" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104230769,"asset_id":104525022,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104230769/download_file?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="104525022"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="104525022"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 104525022; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="3353334" id="multiagentsystems"><div class="js-work-strip profile--work_container" data-work-id="14042212"><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/14042212/Modelling_of_an_agent_based_schedule_optimisation_system"><img alt="Research paper thumbnail of Modelling of an agent-based schedule optimisation system" class="work-thumbnail" src="https://attachments.academia-assets.com/44672334/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/14042212/Modelling_of_an_agent_based_schedule_optimisation_system">Modelling of an agent-based schedule optimisation system</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://profactor.academia.edu/GeorgWeichhart">Georg Weichhart</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://fh-ooe.academia.edu/MichaelAffenzeller">Michael Affenzeller</a></span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper introduces a modelling concept which aims at creating an environment which allows the ...</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 paper introduces a modelling concept which aims at creating an environment which allows the application of global optimisation techniques like Genetic Algorithms in dynamic and complex environments. Such environments are often modelled using multi agent systems. The self-evident drawback of multi agent approaches is that global optimisation techniques are not applicable naturally. The model proposed in this paper divides the overall optimization task into many hierarchically organised small optimization problems and understands the main optimisation objective in the localisation of a 'good' combination of suboptimal solutions of the optimisation tasks in the lower layers of the hierarchy.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5143661a5d53aa7d5b7d9fccaa63e16f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":44672334,"asset_id":14042212,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/44672334/download_file?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="14042212"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="14042212"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 14042212; 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