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He carried out the first full-body simulation of fluid flows over a submarine. His software created the first photorealistic computer graphics in a feature film, "The Last Starfighter", and first combined physical and visual simulation to create the planet Jupiter for "2010". He designed and developed one of the earliest and still most sophisticated artificial life systems, Polyworld. He created the world's first usable handwriting recognition software for second generation Newtons, which became Inkwell in Mac OS X. His studies of neural complexity and topology, using Polyworld, information theory, graph theory, and dynamical systems theory, have provided insight into the role of evolution's influence on complexity, speciation, behavioral adaptation, and neural dynamics.<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://harvard.academia.edu/StevenPinker"><img class="profile-avatar u-positionAbsolute" alt="Steven Pinker" 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/12758/4264/18675036/s200_steven.pinker.jpg" /></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://harvard.academia.edu/StevenPinker">Steven Pinker</a><p class="suggested-user-card__user-info__subheader ds2-5-body-xs">Harvard University</p></div></div><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://williams.academia.edu/StevenJMiller"><img class="profile-avatar u-positionAbsolute" alt="Steven J. 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data-component-name="Pill" data-props="{"color":"gray","children":["Graph Theory"]}" data-trace="false" data-dom-id="Pill-react-component-2f7e01fb-5ccc-490d-bb56-ca79f0ec2cdc"></div> <div id="Pill-react-component-2f7e01fb-5ccc-490d-bb56-ca79f0ec2cdc"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="212979" href="https://www.academia.edu/Documents/in/Genetic_Algorithms"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Genetic Algorithms"]}" data-trace="false" data-dom-id="Pill-react-component-52092cce-4062-4c96-a8b7-f0bdabef55bd"></div> <div id="Pill-react-component-52092cce-4062-4c96-a8b7-f0bdabef55bd"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="212979" href="https://www.academia.edu/Documents/in/Information_Theory"><div class="js-react-on-rails-component" style="display:none" 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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>66</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="Books" data-toggle="tab" href="#books" role="tab" title="Books"><span>1</span> <span class="ds2-5-body-sm-bold">Books</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 Larry Yaeger</h3></div><div class="js-work-strip profile--work_container" data-work-id="103912387"><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/103912387/Transonic_flow_over_afterbodies_including_the_effects_of_jet_plume_and_viscous_interactions_with_separation"><img alt="Research paper thumbnail of Transonic flow over afterbodies including the effects of jet-plume and viscous interactions with separation" 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/103912387/Transonic_flow_over_afterbodies_including_the_effects_of_jet_plume_and_viscous_interactions_with_separation">Transonic flow over afterbodies including the effects of jet-plume and viscous interactions with separation</a></div><div class="wp-workCard_item"><span>15th Aerospace Sciences Meeting</span><span>, 1977</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="103912387"><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="103912387"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 103912387; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=103912387]").text(description); $(".js-view-count[data-work-id=103912387]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 103912387; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='103912387']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=103912387]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":103912387,"title":"Transonic flow over afterbodies including the effects of jet-plume and viscous interactions with 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href="https://www.academia.edu/87605182/Apple_Computer_Inc_One_Infinite_Loop_MS_301_4H_Type_Abstract_Highlight_Terms_Highlight_biological_terms"><img alt="Research paper thumbnail of Apple Computer, Inc.; One Infinite Loop, MS 301-4H Type: Abstract Highlight Terms Highlight biological terms" 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/87605182/Apple_Computer_Inc_One_Infinite_Loop_MS_301_4H_Type_Abstract_Highlight_Terms_Highlight_biological_terms">Apple Computer, Inc.; One Infinite Loop, MS 301-4H Type: Abstract Highlight Terms Highlight biological terms</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper discusses a computer model of living organisms and the ecology they exist in calledPol...</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 discusses a computer model of living organisms and the ecology they exist in calledPolyWorld. PolyWorld attempts to bring together all the principle components of real living systemsinto a single artificial (man-made) living system. PolyWorld brings together biologically motivatedgenetics, simple simulated physiologies and metabolisms, Hebbian learning in arbitrary neural networkarchitectures, a visual perceptive mechanism, and a suite of primitive behaviors in artificial organismsgrounded in an ecology just complex enough ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="87605182"><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="87605182"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 87605182; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=87605182]").text(description); $(".js-view-count[data-work-id=87605182]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 87605182; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='87605182']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=87605182]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":87605182,"title":"Apple Computer, Inc.; One Infinite Loop, MS 301-4H Type: Abstract Highlight Terms Highlight biological terms","internal_url":"https://www.academia.edu/87605182/Apple_Computer_Inc_One_Infinite_Loop_MS_301_4H_Type_Abstract_Highlight_Terms_Highlight_biological_terms","owner_id":212979,"coauthors_can_edit":true,"owner":{"id":212979,"first_name":"Larry","middle_initials":null,"last_name":"Yaeger","page_name":"LarryYaeger","domain_name":"indiana","created_at":"2010-07-05T18:44:10.662-07:00","display_name":"Larry Yaeger","url":"https://indiana.academia.edu/LarryYaeger"},"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="80697864"><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/80697864/I400_I590_Artificial_Life_as_an_approach_to_Artificial_Intelligence"><img alt="Research paper thumbnail of I400/I590 Artificial Life as an approach to Artificial Intelligence" 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/80697864/I400_I590_Artificial_Life_as_an_approach_to_Artificial_Intelligence">I400/I590 Artificial Life as an approach to Artificial Intelligence</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Historically, there have been many legends of living statues, magical pictures, dolls, icons, rob...</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">Historically, there have been many legends of living statues, magical pictures, dolls, icons, robots, and automata that represent or embody the living Not until the electronic age have people attempted to emulate the nervous system While neural activations may be effectively binary, alternative, analog simulations may also have much to contribute to the understanding of living systemsRoss Ashby</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80697864"><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="80697864"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80697864; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80697864]").text(description); $(".js-view-count[data-work-id=80697864]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80697864; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80697864']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=80697864]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80697864,"title":"I400/I590 Artificial Life as an approach to Artificial Intelligence","internal_url":"https://www.academia.edu/80697864/I400_I590_Artificial_Life_as_an_approach_to_Artificial_Intelligence","owner_id":212979,"coauthors_can_edit":true,"owner":{"id":212979,"first_name":"Larry","middle_initials":null,"last_name":"Yaeger","page_name":"LarryYaeger","domain_name":"indiana","created_at":"2010-07-05T18:44:10.662-07:00","display_name":"Larry Yaeger","url":"https://indiana.academia.edu/LarryYaeger"},"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="80697863"><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/80697863/How_evolution_guides_complexity"><img alt="Research paper thumbnail of How evolution guides complexity" class="work-thumbnail" src="https://attachments.academia-assets.com/86994446/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/80697863/How_evolution_guides_complexity">How evolution guides complexity</a></div><div class="wp-workCard_item"><span>HFSP journal</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Long-standing debates about the role of natural selection in the growth of biological complexity ...</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">Long-standing debates about the role of natural selection in the growth of biological complexity over geological time scales are difficult to resolve from the paleobiological record. Using an evolutionary model-a computational ecosystem subjected to natural selection-we investigate evolutionary trends in an information-theoretic measure of the complexity of the neural dynamics of artificial agents inhabiting the model. Our results suggest that evolution always guides complexity change, just not in a single direction. We also demonstrate that neural complexity correlates well with behavioral adaptation but only when complexity increases are achieved through natural selection (as opposed to increases generated randomly or optimized via a genetic algorithm). We conclude with a suggested research direction that might be able to use the artificial neural data generated in these experiments to determine which aspects of network structure give rise to evolutionarily meaningful neural compl...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="64135ee0530706450ee0510ab87f3311" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86994446,"asset_id":80697863,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86994446/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="80697863"><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="80697863"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80697863; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80697863]").text(description); $(".js-view-count[data-work-id=80697863]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80697863; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80697863']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="80697862"><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/80697862/Functional_and_Structural_Topologies_in_Evolved_Neural_Networks"><img alt="Research paper thumbnail of Functional and Structural Topologies in Evolved Neural Networks" class="work-thumbnail" src="https://attachments.academia-assets.com/86994457/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/80697862/Functional_and_Structural_Topologies_in_Evolved_Neural_Networks">Functional and Structural Topologies in Evolved Neural Networks</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The topic of evolutionary trends in complexity has drawn much controversy in the artificial life ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The topic of evolutionary trends in complexity has drawn much controversy in the artificial life community. Rather than investigate the evolution of overall complexity, here we investigate the evolution of topology of networks in the Polyworld artificial life system. Our investigation encompasses both the actual structure of neural networks of agents in this system, and logical or functional networks inferred from statistical dependencies between nodes in the networks. We find interesting trends across several topological measures, which together imply a trend of more integrated activity across the networks (with the networks taking on a more "small-world" character) with evolutionary time.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7bd37e0cbc668256753bc5ff86be92b0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86994457,"asset_id":80697862,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86994457/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="80697862"><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="80697862"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80697862; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80697862]").text(description); $(".js-view-count[data-work-id=80697862]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80697862; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80697862']"); 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: "7bd37e0cbc668256753bc5ff86be92b0" } } $('.js-work-strip[data-work-id=80697862]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80697862,"title":"Functional and Structural Topologies in Evolved Neural Networks","internal_url":"https://www.academia.edu/80697862/Functional_and_Structural_Topologies_in_Evolved_Neural_Networks","owner_id":212979,"coauthors_can_edit":true,"owner":{"id":212979,"first_name":"Larry","middle_initials":null,"last_name":"Yaeger","page_name":"LarryYaeger","domain_name":"indiana","created_at":"2010-07-05T18:44:10.662-07:00","display_name":"Larry Yaeger","url":"https://indiana.academia.edu/LarryYaeger"},"attachments":[{"id":86994457,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86994457/thumbnails/1.jpg","file_name":"LizierEtAl2009_Trends_ECAL.pdf","download_url":"https://www.academia.edu/attachments/86994457/download_file","bulk_download_file_name":"Functional_and_Structural_Topologies_in.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86994457/LizierEtAl2009_Trends_ECAL-libre.pdf?1654374925=\u0026response-content-disposition=attachment%3B+filename%3DFunctional_and_Structural_Topologies_in.pdf\u0026Expires=1739800923\u0026Signature=GKpC5SkVZ4zMGiMDLGtdljOAuTZ3iSucXyMO1iAitWSsnBxaSLj5CnKm9e9AOac729hFpWGMmF7GJVr8dGHx3it-k2m1LGq-mmj7HW8en9~JWd-nWJplEe9Qv6okXK~LY7SuzEyuRQQSw8ZwsaxOaQ7t8amUOV7-GK9AweJK2d9oE-gfl~G9L9Yn4gIGm8-CaxXSH0b8vF-bpG3BJ~cTDSSDf26OPYHCGbIBNH6bgSTGNxOFblcUNPrizPhDo3TGoLcRrxuViNtLnkvbBLeoACJMCVN~PTnewGXcu8j8Z2IT9Jl8OuK9r4m2J8VsIb4H~SRLnCH2u-BXVuIT2OPdjw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="80697861"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/80697861/Evolution_and_Learning"><img alt="Research paper thumbnail of Evolution & Learning" class="work-thumbnail" src="https://attachments.academia-assets.com/86994422/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/80697861/Evolution_and_Learning">Evolution & Learning</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="aeef26defeb4216c614fa8690cd0ca01" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86994422,"asset_id":80697861,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86994422/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="80697861"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="80697861"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80697861; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80697861]").text(description); $(".js-view-count[data-work-id=80697861]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80697861; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80697861']"); 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: "aeef26defeb4216c614fa8690cd0ca01" } } $('.js-work-strip[data-work-id=80697861]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80697861,"title":"Evolution \u0026 Learning","internal_url":"https://www.academia.edu/80697861/Evolution_and_Learning","owner_id":212979,"coauthors_can_edit":true,"owner":{"id":212979,"first_name":"Larry","middle_initials":null,"last_name":"Yaeger","page_name":"LarryYaeger","domain_name":"indiana","created_at":"2010-07-05T18:44:10.662-07:00","display_name":"Larry Yaeger","url":"https://indiana.academia.edu/LarryYaeger"},"attachments":[{"id":86994422,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86994422/thumbnails/1.jpg","file_name":"download.pdf","download_url":"https://www.academia.edu/attachments/86994422/download_file","bulk_download_file_name":"Evolution_and_Learning.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86994422/download-libre.pdf?1654374586=\u0026response-content-disposition=attachment%3B+filename%3DEvolution_and_Learning.pdf\u0026Expires=1739777873\u0026Signature=cST7cPCXJYSRgEsGZy7nc4dmLZJXJYXKwOfySPVA2YaAhctkDz4FRwSnj6KzhvHf7YjSAmxQ1ZWLSzNxlabDE6N1eX3b8BRxHhL~bjlMvGO-c-GuNkdQXb5yfpPK7wLB5nEZaf6LPLc70HEriTuiY5z1r~IGD7onIw71YjpUkvb3O5YWSzqMvYx~06BnbdqMZXRh4nRhqg5wSD8Y1uMWFmNurn8RUlbRD2BWkVKW1JbUwU8C-d0pItJNa1VYOYVL-FlI4-SiCTA0pXCTcLWa7lk35Ifob~Fby6-qaKR8KsMrixcKse3rj8NlH6KYJgLOIecqAQdvK7NQtXX9kE5HDQ__\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="80697860"><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/80697860/Multi_K_Machine_Learning_Ensembles"><img alt="Research paper thumbnail of Multi-K Machine Learning Ensembles" class="work-thumbnail" src="https://attachments.academia-assets.com/86994469/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/80697860/Multi_K_Machine_Learning_Ensembles">Multi-K Machine Learning Ensembles</a></div><div class="wp-workCard_item"><span>Midwest Artificial Intelligence and Cognitive Science Conference</span><span>, Apr 21, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Ensemble machine learning models often surpass single models in classification accuracy at the ex...</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">Ensemble machine learning models often surpass single models in classification accuracy at the expense of higher computational requirements during training and execution. In this paper we present a novel ensemble algorithm called Multi-K which uses unsupervised clustering as a form of dataset preprocessing to create component models that lead to effective and efficient ensembles. We also present a modification of Multi-K that we call Multi-KX that incorporates a metalearner to help with ensemble classifications. We ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="76b6ab164674f3fa3119470d4150c9da" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86994469,"asset_id":80697860,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86994469/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="80697860"><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="80697860"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80697860; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80697860]").text(description); $(".js-view-count[data-work-id=80697860]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80697860; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80697860']"); 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: "76b6ab164674f3fa3119470d4150c9da" } } $('.js-work-strip[data-work-id=80697860]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80697860,"title":"Multi-K Machine Learning Ensembles","internal_url":"https://www.academia.edu/80697860/Multi_K_Machine_Learning_Ensembles","owner_id":212979,"coauthors_can_edit":true,"owner":{"id":212979,"first_name":"Larry","middle_initials":null,"last_name":"Yaeger","page_name":"LarryYaeger","domain_name":"indiana","created_at":"2010-07-05T18:44:10.662-07:00","display_name":"Larry Yaeger","url":"https://indiana.academia.edu/LarryYaeger"},"attachments":[{"id":86994469,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/86994469/thumbnails/1.jpg","file_name":"submission_12.pdf","download_url":"https://www.academia.edu/attachments/86994469/download_file","bulk_download_file_name":"Multi_K_Machine_Learning_Ensembles.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/86994469/submission_12-libre.pdf?1654374923=\u0026response-content-disposition=attachment%3B+filename%3DMulti_K_Machine_Learning_Ensembles.pdf\u0026Expires=1739800923\u0026Signature=STZPsAEfLiQnxwyugPEIuWhNoWMN5W2S4HD5HuhO5OLC8PLLkx6JTJbWXYTeu89QQsJqGWh214mEWpS0zxkAglOOpsEYkiAc4II73b21WOQn2dZ~X9mq6P95yn7NRasiitQtN6tcWD5MwK0p3SxPK9LlwmRtMf36~DO7geR563vHdbFp8BN0z5VQ~MKJ0B7qN96~JY6IrxsLRGRzKZaZXfVxc~ojt00abiXmmelI5OqcuyYdr-5X4DG8rZ2K7kxCr~obq6gQcz3FrBFhIPzMOYnFaGVZ6LocZnjcwFZTwz~mMZA7LopemUzCIFUPvdBmWiLyZUW2AbsCzfrx8ArqWA__\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="80697679"><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/80697679/Genetic_clustering_for_the_identification_of_species"><img alt="Research paper thumbnail of Genetic clustering for the identification of species" class="work-thumbnail" src="https://attachments.academia-assets.com/86994330/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/80697679/Genetic_clustering_for_the_identification_of_species">Genetic clustering for the identification of species</a></div><div class="wp-workCard_item"><span>Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Artificial life simulations can yield distinct populations of agents representing different adapt...</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">Artificial life simulations can yield distinct populations of agents representing different adaptations to a common environment or specialized adaptations to different environments. Here we apply a standard clustering algorithm to the genomes of such agents to discover and characterize these subpopulations. As evolution proceeds new subpopulations are produced, which show up as new clusters. Cluster centroids allow us to characterize these different subpopulations and identify their distinct adaptation mechanisms. We suggest these subpopulations may reasonably be thought of as species, even if the simulation software allows interbreeding between members of the different subpopulations. Our results indicate both sympatric and allopatric speciation are present in the Polyworld artificial life system. Our analysis suggests that intra-and inter-cluster fecundity differences may be sufficient to foster sympatric speciation in artificial and biological ecosystems.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c5a61d68da48cad57eae15811fc263d7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86994330,"asset_id":80697679,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86994330/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="80697679"><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="80697679"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80697679; 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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="74826948"><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/74826948/Evolutionary_Selection_of_Network_Structure_and_Function"><img alt="Research paper thumbnail of Evolutionary Selection of Network Structure and Function" 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/74826948/Evolutionary_Selection_of_Network_Structure_and_Function">Evolutionary Selection of Network Structure and Function</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We explore the relationship between evolved neural network structure and function, by applying gr...</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 explore the relationship between evolved neural network structure and function, by applying graph theoretical tool s to the analysis of the topology of artificial neural networks known to exhibit evolutionary increases in dynamical neural complexity. Our results suggest a synergistic convergence between network structures emerging due to physical constraints, such as wiring length and brain volume, and optimal network topologies evolved purely for function in the absence of physical constraints. We observe increases in clusterin g coefficients in concert with decreases in path lengths that together produce a driven evolutionary bias towards smallworld networks relative to comparable networks in a passive null model. These small-world biases are exhibited during the same periods that evolution actively selects for increa sing neural complexity (also during which the model’s agents are behaviorally adapting to their environment), thus strengt hening the association between small-wo...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="74826948"><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="74826948"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 74826948; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=74826948]").text(description); $(".js-view-count[data-work-id=74826948]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 74826948; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='74826948']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=74826948]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":74826948,"title":"Evolutionary Selection of Network Structure and Function","internal_url":"https://www.academia.edu/74826948/Evolutionary_Selection_of_Network_Structure_and_Function","owner_id":212979,"coauthors_can_edit":true,"owner":{"id":212979,"first_name":"Larry","middle_initials":null,"last_name":"Yaeger","page_name":"LarryYaeger","domain_name":"indiana","created_at":"2010-07-05T18:44:10.662-07:00","display_name":"Larry Yaeger","url":"https://indiana.academia.edu/LarryYaeger"},"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="69815902"><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/69815902/Evolution_of_Neural_Dynamics_in_an_Ecological_Model"><img alt="Research paper thumbnail of Evolution of Neural Dynamics in an Ecological Model" class="work-thumbnail" src="https://attachments.academia-assets.com/79766620/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/69815902/Evolution_of_Neural_Dynamics_in_an_Ecological_Model">Evolution of Neural Dynamics in an Ecological Model</a></div><div class="wp-workCard_item"><span>Geosciences</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">What is the optimal level of chaos in a computational system? If a system is too chaotic, it cann...</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">What is the optimal level of chaos in a computational system? If a system is too chaotic, it cannot reliably store information. If it is too ordered, it cannot transmit information. A variety of computational systems exhibit dynamics at the “edge of chaos”, the transition between the ordered and chaotic regimes. In this work, we examine the evolved neural networks of Polyworld, an artificial life model consisting of a simulated ecology populated with biologically inspired agents. As these agents adapt to their environment, their initially simple neural networks become increasingly capable of exhibiting rich dynamics. Dynamical systems analysis reveals that natural selection drives these networks toward the edge of chaos until the agent population is able to sustain itself. After this point, the evolutionary trend stabilizes, with neural dynamics remaining on average significantly far from the transition to chaos.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7d8cbdb8988ff991677ccc8d11283f8c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":79766620,"asset_id":69815902,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/79766620/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="69815902"><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="69815902"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 69815902; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=69815902]").text(description); $(".js-view-count[data-work-id=69815902]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 69815902; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='69815902']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(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="50120950"><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/50120950/Representing_and_Incorporating_Prior_Knowledge_in_Neural_Network_Training"><img alt="Research paper thumbnail of Representing and Incorporating Prior Knowledge in Neural Network Training" 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/50120950/Representing_and_Incorporating_Prior_Knowledge_in_Neural_Network_Training">Representing and Incorporating Prior Knowledge in Neural Network Training</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2012</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT The present section focuses on tricks for four important aspects in learning: (1) incorp...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">ABSTRACT The present section focuses on tricks for four important aspects in learning: (1) incorporation of prior knowledge, (2) choice of representation for the learning task, (3) unequal class prior distributions, and finally (4) large network training.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="50120950"><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="50120950"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50120950; 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A second-order accurate finite-difference marching technique was used to integrate Euler&amp;amp;amp;#x27;s equations, and analytic conformal mappings were employed to develop a computational grid. All shocks in the flow field are treated explicitly as discontinuities satisfying the Rankine-Hugoniot jump conditions. Computational difficulties associated with blunt-nose entropy layers are avoided by explicitly following a stream ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="50120948"><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="50120948"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50120948; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=50120948]").text(description); $(".js-view-count[data-work-id=50120948]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 50120948; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='50120948']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=50120948]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":50120948,"title":"Computation of High-Speed Inviscid Flows about Real Configurations","internal_url":"https://www.academia.edu/50120948/Computation_of_High_Speed_Inviscid_Flows_about_Real_Configurations","owner_id":212979,"coauthors_can_edit":true,"owner":{"id":212979,"first_name":"Larry","middle_initials":null,"last_name":"Yaeger","page_name":"LarryYaeger","domain_name":"indiana","created_at":"2010-07-05T18:44:10.662-07:00","display_name":"Larry Yaeger","url":"https://indiana.academia.edu/LarryYaeger"},"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="50120947"><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/50120947/Method_for_training_an_adaptive_statistical_classifier_with_improved_learning_of_difficult_samples"><img alt="Research paper thumbnail of Method for training an adaptive statistical classifier with improved learning of difficult samples" 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/50120947/Method_for_training_an_adaptive_statistical_classifier_with_improved_learning_of_difficult_samples">Method for training an adaptive statistical classifier with improved learning of difficult samples</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">[57] ABSTRACT A statistical classifier that can be used for pattern recognition is trained to rec...</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">[57] ABSTRACT A statistical classifier that can be used for pattern recognition is trained to recognize negative, or improper patterns as well as proper patterns that are positively associated with desired output classes. A set of training samples includes both the negative and positive patterns, and target output values for the negative patterns are set so that no recognized class is indicated. 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</script> <div class="js-work-strip profile--work_container" data-work-id="50120945"><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/50120945/Adaptive_statistical_classifier_which_provides_reliable_estimates_or_output_classes_having_low_probabilities"><img alt="Research paper thumbnail of Adaptive statistical classifier which provides reliable estimates or output classes having low probabilities" 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/50120945/Adaptive_statistical_classifier_which_provides_reliable_estimates_or_output_classes_having_low_probabilities">Adaptive statistical classifier which provides reliable estimates or output classes having low probabilities</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">[57] ABSTRACT A statistical classifier for pattern recognition, such as a neural network, produce...</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">[57] ABSTRACT A statistical classifier for pattern recognition, such as a neural network, produces a plurality of output signals corresponding to the probabilities that a given input pattern belongs in respective classes. The classifier is trained in a manner such that low probabilities which pertain to classes of interest are not suppressed too greatly. This is achieved by modifying the amount by which error signals, corresponding to classes which are incorrectly identified, are employed in the training process, relative to error signals ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="50120945"><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="50120945"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50120945; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=50120945]").text(description); $(".js-view-count[data-work-id=50120945]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 50120945; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='50120945']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=50120945]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":50120945,"title":"Adaptive statistical classifier which provides reliable estimates or output classes having low probabilities","internal_url":"https://www.academia.edu/50120945/Adaptive_statistical_classifier_which_provides_reliable_estimates_or_output_classes_having_low_probabilities","owner_id":212979,"coauthors_can_edit":true,"owner":{"id":212979,"first_name":"Larry","middle_initials":null,"last_name":"Yaeger","page_name":"LarryYaeger","domain_name":"indiana","created_at":"2010-07-05T18:44:10.662-07:00","display_name":"Larry Yaeger","url":"https://indiana.academia.edu/LarryYaeger"},"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="50120944"><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/50120944/Visualization_of_natural_phenomena_Robert_S_Wolff_Larry_Yaeger"><img alt="Research paper thumbnail of Visualization of natural phenomena / Robert S. Wolff, Larry Yaeger" 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/50120944/Visualization_of_natural_phenomena_Robert_S_Wolff_Larry_Yaeger">Visualization of natural phenomena / Robert S. Wolff, Larry Yaeger</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">&quot;Systems requeriments for computer laser optical disc : Macintosh II or laser 8 MB RAM Syste...</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">&quot;Systems requeriments for computer laser optical disc : Macintosh II or laser 8 MB RAM System 7.01 or 7.1 hard disk driver with 5MB free space 13 in RGB monitor (8 bit color minimum) CD-ROM player quicktime 1.5 or later needed to play movies&quot; Incluye bibliografía e índice</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="50120944"><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="50120944"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50120944; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=50120944]").text(description); $(".js-view-count[data-work-id=50120944]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 50120944; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='50120944']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=50120944]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":50120944,"title":"Visualization of natural phenomena / Robert S. Wolff, Larry Yaeger","internal_url":"https://www.academia.edu/50120944/Visualization_of_natural_phenomena_Robert_S_Wolff_Larry_Yaeger","owner_id":212979,"coauthors_can_edit":true,"owner":{"id":212979,"first_name":"Larry","middle_initials":null,"last_name":"Yaeger","page_name":"LarryYaeger","domain_name":"indiana","created_at":"2010-07-05T18:44:10.662-07:00","display_name":"Larry Yaeger","url":"https://indiana.academia.edu/LarryYaeger"},"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="50120943"><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/50120943/Fifth_International_Conference_on_Microelectronics_for_Neural_Networks_and_Fuzzy_Systems_Lausanne_Switzerland_Feb_12_14_1996_IEEE_Computer_Society_Press_Reprint"><img alt="Research paper thumbnail of Fifth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, Lausanne, Switzerland, Feb. 12--14, 1996. IEEE Computer Society Press. --- Reprint" class="work-thumbnail" src="https://attachments.academia-assets.com/68225047/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/50120943/Fifth_International_Conference_on_Microelectronics_for_Neural_Networks_and_Fuzzy_Systems_Lausanne_Switzerland_Feb_12_14_1996_IEEE_Computer_Society_Press_Reprint">Fifth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, Lausanne, Switzerland, Feb. 12--14, 1996. IEEE Computer Society Press. --- Reprint</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The need for fast and accurate text entry on small handheld computers has led to a resurgence of ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The need for fast and accurate text entry on small handheld computers has led to a resurgence of interest in on-line word recognition using artificial neural networks. Classical methods have been combined and improved to produce robust recognition of hand-printed English text. The central concept of a neural net as a character classifier provides a good base for a recognition system; long-standing issues relative to training, generalization, segmentation, probabilistic formalisms, etc., need to resolved, however, to get excellent performance. A number of innovations in how to use a neural net as a classifier in a word recognizer are presented: negative training, stroke warping, balancing, normalized output error, error emphasis, multiple representations, quantized weights, and integrated word segmentation all contribute to efficient and robust performance. 1: Introduction While on-line handwriting recognition is a problem of long-standing interest and activity, the recent introducti...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8868e7db6c079465847528dd776c4465" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":68225047,"asset_id":50120943,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/68225047/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="50120943"><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="50120943"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50120943; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=50120943]").text(description); $(".js-view-count[data-work-id=50120943]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 50120943; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='50120943']"); 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: "8868e7db6c079465847528dd776c4465" } } $('.js-work-strip[data-work-id=50120943]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":50120943,"title":"Fifth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, Lausanne, Switzerland, Feb. 12--14, 1996. 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Conference on the Simulation and Synthesis of Living Systems" 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/50120942/Artificial_Life_10_Proceedings_of_the_10_th_Int_Conference_on_the_Simulation_and_Synthesis_of_Living_Systems">Artificial Life 10: Proceedings of the 10 th Int. Conference on the Simulation and Synthesis of Living Systems</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="50120942"><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="50120942"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50120942; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=50120942]").text(description); $(".js-view-count[data-work-id=50120942]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 50120942; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='50120942']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=50120942]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":50120942,"title":"Artificial Life 10: Proceedings of the 10 th Int. Conference on the Simulation and Synthesis of Living Systems","internal_url":"https://www.academia.edu/50120942/Artificial_Life_10_Proceedings_of_the_10_th_Int_Conference_on_the_Simulation_and_Synthesis_of_Living_Systems","owner_id":212979,"coauthors_can_edit":true,"owner":{"id":212979,"first_name":"Larry","middle_initials":null,"last_name":"Yaeger","page_name":"LarryYaeger","domain_name":"indiana","created_at":"2010-07-05T18:44:10.662-07:00","display_name":"Larry Yaeger","url":"https://indiana.academia.edu/LarryYaeger"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="50120941"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/50120941/Visualization_of_Natural_Phaenomena"><img alt="Research paper thumbnail of Visualization of Natural Phaenomena" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/50120941/Visualization_of_Natural_Phaenomena">Visualization of Natural Phaenomena</a></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="50120941"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="50120941"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50120941; 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=103912387]").text(description); $(".js-view-count[data-work-id=103912387]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 103912387; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='103912387']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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</script> <div class="js-work-strip profile--work_container" data-work-id="87605182"><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/87605182/Apple_Computer_Inc_One_Infinite_Loop_MS_301_4H_Type_Abstract_Highlight_Terms_Highlight_biological_terms"><img alt="Research paper thumbnail of Apple Computer, Inc.; One Infinite Loop, MS 301-4H Type: Abstract Highlight Terms Highlight biological terms" 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/87605182/Apple_Computer_Inc_One_Infinite_Loop_MS_301_4H_Type_Abstract_Highlight_Terms_Highlight_biological_terms">Apple Computer, Inc.; One Infinite Loop, MS 301-4H Type: Abstract Highlight Terms Highlight biological terms</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">This paper discusses a computer model of living organisms and the ecology they exist in calledPol...</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 discusses a computer model of living organisms and the ecology they exist in calledPolyWorld. PolyWorld attempts to bring together all the principle components of real living systemsinto a single artificial (man-made) living system. PolyWorld brings together biologically motivatedgenetics, simple simulated physiologies and metabolisms, Hebbian learning in arbitrary neural networkarchitectures, a visual perceptive mechanism, and a suite of primitive behaviors in artificial organismsgrounded in an ecology just complex enough ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="87605182"><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="87605182"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 87605182; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=87605182]").text(description); $(".js-view-count[data-work-id=87605182]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 87605182; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='87605182']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=80697864]").text(description); $(".js-view-count[data-work-id=80697864]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 80697864; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='80697864']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=80697864]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":80697864,"title":"I400/I590 Artificial Life as an approach to Artificial Intelligence","internal_url":"https://www.academia.edu/80697864/I400_I590_Artificial_Life_as_an_approach_to_Artificial_Intelligence","owner_id":212979,"coauthors_can_edit":true,"owner":{"id":212979,"first_name":"Larry","middle_initials":null,"last_name":"Yaeger","page_name":"LarryYaeger","domain_name":"indiana","created_at":"2010-07-05T18:44:10.662-07:00","display_name":"Larry Yaeger","url":"https://indiana.academia.edu/LarryYaeger"},"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="80697863"><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/80697863/How_evolution_guides_complexity"><img alt="Research paper thumbnail of How evolution guides complexity" class="work-thumbnail" src="https://attachments.academia-assets.com/86994446/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/80697863/How_evolution_guides_complexity">How evolution guides complexity</a></div><div class="wp-workCard_item"><span>HFSP journal</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Long-standing debates about the role of natural selection in the growth of biological complexity ...</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">Long-standing debates about the role of natural selection in the growth of biological complexity over geological time scales are difficult to resolve from the paleobiological record. Using an evolutionary model-a computational ecosystem subjected to natural selection-we investigate evolutionary trends in an information-theoretic measure of the complexity of the neural dynamics of artificial agents inhabiting the model. Our results suggest that evolution always guides complexity change, just not in a single direction. We also demonstrate that neural complexity correlates well with behavioral adaptation but only when complexity increases are achieved through natural selection (as opposed to increases generated randomly or optimized via a genetic algorithm). We conclude with a suggested research direction that might be able to use the artificial neural data generated in these experiments to determine which aspects of network structure give rise to evolutionarily meaningful neural compl...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="64135ee0530706450ee0510ab87f3311" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86994446,"asset_id":80697863,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86994446/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="80697863"><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="80697863"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80697863; 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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="80697862"><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/80697862/Functional_and_Structural_Topologies_in_Evolved_Neural_Networks"><img alt="Research paper thumbnail of Functional and Structural Topologies in Evolved Neural Networks" class="work-thumbnail" src="https://attachments.academia-assets.com/86994457/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/80697862/Functional_and_Structural_Topologies_in_Evolved_Neural_Networks">Functional and Structural Topologies in Evolved Neural Networks</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The topic of evolutionary trends in complexity has drawn much controversy in the artificial life ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The topic of evolutionary trends in complexity has drawn much controversy in the artificial life community. Rather than investigate the evolution of overall complexity, here we investigate the evolution of topology of networks in the Polyworld artificial life system. Our investigation encompasses both the actual structure of neural networks of agents in this system, and logical or functional networks inferred from statistical dependencies between nodes in the networks. We find interesting trends across several topological measures, which together imply a trend of more integrated activity across the networks (with the networks taking on a more "small-world" character) with evolutionary time.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7bd37e0cbc668256753bc5ff86be92b0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86994457,"asset_id":80697862,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86994457/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="80697862"><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="80697862"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80697862; 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In this paper we present a novel ensemble algorithm called Multi-K which uses unsupervised clustering as a form of dataset preprocessing to create component models that lead to effective and efficient ensembles. We also present a modification of Multi-K that we call Multi-KX that incorporates a metalearner to help with ensemble classifications. We ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="76b6ab164674f3fa3119470d4150c9da" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86994469,"asset_id":80697860,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86994469/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="80697860"><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="80697860"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80697860; 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$(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="80697679"><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/80697679/Genetic_clustering_for_the_identification_of_species"><img alt="Research paper thumbnail of Genetic clustering for the identification of species" class="work-thumbnail" src="https://attachments.academia-assets.com/86994330/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/80697679/Genetic_clustering_for_the_identification_of_species">Genetic clustering for the identification of species</a></div><div class="wp-workCard_item"><span>Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Artificial life simulations can yield distinct populations of agents representing different adapt...</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">Artificial life simulations can yield distinct populations of agents representing different adaptations to a common environment or specialized adaptations to different environments. Here we apply a standard clustering algorithm to the genomes of such agents to discover and characterize these subpopulations. As evolution proceeds new subpopulations are produced, which show up as new clusters. Cluster centroids allow us to characterize these different subpopulations and identify their distinct adaptation mechanisms. We suggest these subpopulations may reasonably be thought of as species, even if the simulation software allows interbreeding between members of the different subpopulations. Our results indicate both sympatric and allopatric speciation are present in the Polyworld artificial life system. Our analysis suggests that intra-and inter-cluster fecundity differences may be sufficient to foster sympatric speciation in artificial and biological ecosystems.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c5a61d68da48cad57eae15811fc263d7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":86994330,"asset_id":80697679,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/86994330/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="80697679"><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="80697679"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 80697679; 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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="74826948"><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/74826948/Evolutionary_Selection_of_Network_Structure_and_Function"><img alt="Research paper thumbnail of Evolutionary Selection of Network Structure and Function" 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/74826948/Evolutionary_Selection_of_Network_Structure_and_Function">Evolutionary Selection of Network Structure and Function</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We explore the relationship between evolved neural network structure and function, by applying gr...</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 explore the relationship between evolved neural network structure and function, by applying graph theoretical tool s to the analysis of the topology of artificial neural networks known to exhibit evolutionary increases in dynamical neural complexity. Our results suggest a synergistic convergence between network structures emerging due to physical constraints, such as wiring length and brain volume, and optimal network topologies evolved purely for function in the absence of physical constraints. We observe increases in clusterin g coefficients in concert with decreases in path lengths that together produce a driven evolutionary bias towards smallworld networks relative to comparable networks in a passive null model. These small-world biases are exhibited during the same periods that evolution actively selects for increa sing neural complexity (also during which the model’s agents are behaviorally adapting to their environment), thus strengt hening the association between small-wo...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="74826948"><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="74826948"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 74826948; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=74826948]").text(description); $(".js-view-count[data-work-id=74826948]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 74826948; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='74826948']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=74826948]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":74826948,"title":"Evolutionary Selection of Network Structure and Function","internal_url":"https://www.academia.edu/74826948/Evolutionary_Selection_of_Network_Structure_and_Function","owner_id":212979,"coauthors_can_edit":true,"owner":{"id":212979,"first_name":"Larry","middle_initials":null,"last_name":"Yaeger","page_name":"LarryYaeger","domain_name":"indiana","created_at":"2010-07-05T18:44:10.662-07:00","display_name":"Larry Yaeger","url":"https://indiana.academia.edu/LarryYaeger"},"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="69815902"><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/69815902/Evolution_of_Neural_Dynamics_in_an_Ecological_Model"><img alt="Research paper thumbnail of Evolution of Neural Dynamics in an Ecological Model" class="work-thumbnail" src="https://attachments.academia-assets.com/79766620/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/69815902/Evolution_of_Neural_Dynamics_in_an_Ecological_Model">Evolution of Neural Dynamics in an Ecological Model</a></div><div class="wp-workCard_item"><span>Geosciences</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">What is the optimal level of chaos in a computational system? If a system is too chaotic, it cann...</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">What is the optimal level of chaos in a computational system? If a system is too chaotic, it cannot reliably store information. If it is too ordered, it cannot transmit information. A variety of computational systems exhibit dynamics at the “edge of chaos”, the transition between the ordered and chaotic regimes. In this work, we examine the evolved neural networks of Polyworld, an artificial life model consisting of a simulated ecology populated with biologically inspired agents. As these agents adapt to their environment, their initially simple neural networks become increasingly capable of exhibiting rich dynamics. Dynamical systems analysis reveals that natural selection drives these networks toward the edge of chaos until the agent population is able to sustain itself. After this point, the evolutionary trend stabilizes, with neural dynamics remaining on average significantly far from the transition to chaos.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7d8cbdb8988ff991677ccc8d11283f8c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":79766620,"asset_id":69815902,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/79766620/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="69815902"><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="69815902"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 69815902; 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A second-order accurate finite-difference marching technique was used to integrate Euler&amp;amp;amp;#x27;s equations, and analytic conformal mappings were employed to develop a computational grid. All shocks in the flow field are treated explicitly as discontinuities satisfying the Rankine-Hugoniot jump conditions. Computational difficulties associated with blunt-nose entropy layers are avoided by explicitly following a stream ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="50120948"><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="50120948"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50120948; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=50120948]").text(description); $(".js-view-count[data-work-id=50120948]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 50120948; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='50120948']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=50120948]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":50120948,"title":"Computation of High-Speed Inviscid Flows about Real Configurations","internal_url":"https://www.academia.edu/50120948/Computation_of_High_Speed_Inviscid_Flows_about_Real_Configurations","owner_id":212979,"coauthors_can_edit":true,"owner":{"id":212979,"first_name":"Larry","middle_initials":null,"last_name":"Yaeger","page_name":"LarryYaeger","domain_name":"indiana","created_at":"2010-07-05T18:44:10.662-07:00","display_name":"Larry Yaeger","url":"https://indiana.academia.edu/LarryYaeger"},"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="50120947"><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/50120947/Method_for_training_an_adaptive_statistical_classifier_with_improved_learning_of_difficult_samples"><img alt="Research paper thumbnail of Method for training an adaptive statistical classifier with improved learning of difficult samples" 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/50120947/Method_for_training_an_adaptive_statistical_classifier_with_improved_learning_of_difficult_samples">Method for training an adaptive statistical classifier with improved learning of difficult samples</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">[57] ABSTRACT A statistical classifier that can be used for pattern recognition is trained to rec...</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">[57] ABSTRACT A statistical classifier that can be used for pattern recognition is trained to recognize negative, or improper patterns as well as proper patterns that are positively associated with desired output classes. A set of training samples includes both the negative and positive patterns, and target output values for the negative patterns are set so that no recognized class is indicated. The negative patterns are selected for training with less frequency than the positive patterns, and their effect on training is also modified, so that ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="50120947"><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="50120947"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50120947; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); 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</script> <div class="js-work-strip profile--work_container" data-work-id="50120945"><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/50120945/Adaptive_statistical_classifier_which_provides_reliable_estimates_or_output_classes_having_low_probabilities"><img alt="Research paper thumbnail of Adaptive statistical classifier which provides reliable estimates or output classes having low probabilities" 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/50120945/Adaptive_statistical_classifier_which_provides_reliable_estimates_or_output_classes_having_low_probabilities">Adaptive statistical classifier which provides reliable estimates or output classes having low probabilities</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">[57] ABSTRACT A statistical classifier for pattern recognition, such as a neural network, produce...</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">[57] ABSTRACT A statistical classifier for pattern recognition, such as a neural network, produces a plurality of output signals corresponding to the probabilities that a given input pattern belongs in respective classes. The classifier is trained in a manner such that low probabilities which pertain to classes of interest are not suppressed too greatly. This is achieved by modifying the amount by which error signals, corresponding to classes which are incorrectly identified, are employed in the training process, relative to error signals ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="50120945"><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="50120945"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50120945; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=50120945]").text(description); $(".js-view-count[data-work-id=50120945]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 50120945; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='50120945']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=50120945]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":50120945,"title":"Adaptive statistical classifier which provides reliable estimates or output classes having low probabilities","internal_url":"https://www.academia.edu/50120945/Adaptive_statistical_classifier_which_provides_reliable_estimates_or_output_classes_having_low_probabilities","owner_id":212979,"coauthors_can_edit":true,"owner":{"id":212979,"first_name":"Larry","middle_initials":null,"last_name":"Yaeger","page_name":"LarryYaeger","domain_name":"indiana","created_at":"2010-07-05T18:44:10.662-07:00","display_name":"Larry Yaeger","url":"https://indiana.academia.edu/LarryYaeger"},"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="50120944"><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/50120944/Visualization_of_natural_phenomena_Robert_S_Wolff_Larry_Yaeger"><img alt="Research paper thumbnail of Visualization of natural phenomena / Robert S. Wolff, Larry Yaeger" 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/50120944/Visualization_of_natural_phenomena_Robert_S_Wolff_Larry_Yaeger">Visualization of natural phenomena / Robert S. Wolff, Larry Yaeger</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">&quot;Systems requeriments for computer laser optical disc : Macintosh II or laser 8 MB RAM Syste...</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">&quot;Systems requeriments for computer laser optical disc : Macintosh II or laser 8 MB RAM System 7.01 or 7.1 hard disk driver with 5MB free space 13 in RGB monitor (8 bit color minimum) CD-ROM player quicktime 1.5 or later needed to play movies&quot; Incluye bibliografía e índice</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="50120944"><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="50120944"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50120944; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=50120944]").text(description); $(".js-view-count[data-work-id=50120944]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 50120944; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='50120944']"); 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 (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=50120944]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":50120944,"title":"Visualization of natural phenomena / Robert S. Wolff, Larry Yaeger","internal_url":"https://www.academia.edu/50120944/Visualization_of_natural_phenomena_Robert_S_Wolff_Larry_Yaeger","owner_id":212979,"coauthors_can_edit":true,"owner":{"id":212979,"first_name":"Larry","middle_initials":null,"last_name":"Yaeger","page_name":"LarryYaeger","domain_name":"indiana","created_at":"2010-07-05T18:44:10.662-07:00","display_name":"Larry Yaeger","url":"https://indiana.academia.edu/LarryYaeger"},"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="50120943"><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/50120943/Fifth_International_Conference_on_Microelectronics_for_Neural_Networks_and_Fuzzy_Systems_Lausanne_Switzerland_Feb_12_14_1996_IEEE_Computer_Society_Press_Reprint"><img alt="Research paper thumbnail of Fifth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, Lausanne, Switzerland, Feb. 12--14, 1996. IEEE Computer Society Press. --- Reprint" class="work-thumbnail" src="https://attachments.academia-assets.com/68225047/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/50120943/Fifth_International_Conference_on_Microelectronics_for_Neural_Networks_and_Fuzzy_Systems_Lausanne_Switzerland_Feb_12_14_1996_IEEE_Computer_Society_Press_Reprint">Fifth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, Lausanne, Switzerland, Feb. 12--14, 1996. IEEE Computer Society Press. --- Reprint</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The need for fast and accurate text entry on small handheld computers has led to a resurgence of ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The need for fast and accurate text entry on small handheld computers has led to a resurgence of interest in on-line word recognition using artificial neural networks. Classical methods have been combined and improved to produce robust recognition of hand-printed English text. The central concept of a neural net as a character classifier provides a good base for a recognition system; long-standing issues relative to training, generalization, segmentation, probabilistic formalisms, etc., need to resolved, however, to get excellent performance. A number of innovations in how to use a neural net as a classifier in a word recognizer are presented: negative training, stroke warping, balancing, normalized output error, error emphasis, multiple representations, quantized weights, and integrated word segmentation all contribute to efficient and robust performance. 1: Introduction While on-line handwriting recognition is a problem of long-standing interest and activity, the recent introducti...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="8868e7db6c079465847528dd776c4465" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":68225047,"asset_id":50120943,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/68225047/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="50120943"><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="50120943"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 50120943; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=50120943]").text(description); $(".js-view-count[data-work-id=50120943]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 50120943; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='50120943']"); 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: "8868e7db6c079465847528dd776c4465" } } $('.js-work-strip[data-work-id=50120943]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":50120943,"title":"Fifth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, Lausanne, Switzerland, Feb. 12--14, 1996. 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These neural graphs and the neural dynamics operating during the agents' lives are recorded and analyzed using graph theory and information theoretic measures of complexity, respectively. This reveals a significant evolutionary pressure towards increasing complexity and small-world graphs during periods of behavioral adaptation to the environment, followed by a plateauing in these trends once the agents become "good enough" at survival and reproduction. However, since other agents are the most complex and challenging aspect of the "environment" for any given agent, one also observes subsequent jumps in complexity in a very recognizable form of punctuated equilibrium. By applying both graph and information theory we are able to identify trends in network topology that foster dynamical complexity, suggesting that small-world networks operating close to the edge of chaos yield the greatest complexity, consistent with data suggesting biological brains operate near criticality.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d38ce693009ce73ba4eebc29545c971f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":57116776,"asset_id":37165626,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/57116776/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="37165626"><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="37165626"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 37165626; 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