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class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Laetitia Jourdan</h3></div><div class="js-work-strip profile--work_container" data-work-id="94148547"><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/94148547/Automatic_Configuration_of_Bi_Objective_Optimisation_Algorithms_Impact_of_Correlation_Between_Objectives"><img alt="Research paper thumbnail of Automatic Configuration of Bi-Objective Optimisation Algorithms: Impact of Correlation Between Objectives" class="work-thumbnail" src="https://attachments.academia-assets.com/96686830/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/94148547/Automatic_Configuration_of_Bi_Objective_Optimisation_Algorithms_Impact_of_Correlation_Between_Objectives">Automatic Configuration of Bi-Objective Optimisation Algorithms: Impact of Correlation Between Objectives</a></div><div class="wp-workCard_item"><span>2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI)</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="23346a07035c6535a897275fbbdc26fb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96686830,"asset_id":94148547,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96686830/download_file?st=MTczMjcyNjUwNCw4LjIyMi4yMDguMTQ2&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 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text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/94148546/Multi_objective_recommender_system_for_corporate_MOOC">Multi-objective recommender system for corporate MOOC</a></div><div class="wp-workCard_item"><span>Proceedings of the Genetic and Evolutionary Computation Conference Companion</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="68117bc921a0a8d1964dcbfecd783499" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96686710,"asset_id":94148546,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96686710/download_file?st=MTczMjcyNjUwNCw4LjIyMi4yMDguMTQ2&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 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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="94148528"><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/94148528/Decoder_based_evolutionary_algorithm_for_bi_objective_just_in_time_single_machine_job_shop"><img alt="Research paper thumbnail of Decoder-based evolutionary algorithm for bi-objective just-in-time single-machine job-shop" class="work-thumbnail" src="https://attachments.academia-assets.com/96686819/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/94148528/Decoder_based_evolutionary_algorithm_for_bi_objective_just_in_time_single_machine_job_shop">Decoder-based evolutionary algorithm for bi-objective just-in-time single-machine job-shop</a></div><div class="wp-workCard_item"><span>2016 IEEE Symposium Series on Computational Intelligence (SSCI)</span><span>, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The bi-objective just-in-time single-machine job-shop scheduling problem (JIT-JSP) aims at simult...</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 bi-objective just-in-time single-machine job-shop scheduling problem (JIT-JSP) aims at simultaneously minimizing earliness and tardiness. In this paper, a multi-objective decoder-based evolutionary algorithm is proposed. The decoding strategy divides the search into two steps. In the first step, the search of the permutation order of the jobs is realized thanks to a multi-objective evolutionary algorithm. For a fixed permutation, the decoder algorithm optimizes the multi-objective timing sub-problem in the second step. Thus each permutation order induces a Pareto set of solutions. Two different decoding strategies to fix the idle times are proposed, one approximate and one exact. A comparison study with a classical multi-objective evolutionary algorithm underlines the performance of the proposed decoding strategy and the interest of the approximate decoder.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="df4b8114527ed6ca45eaf97fb67f80c4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96686819,"asset_id":94148528,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96686819/download_file?st=MTczMjcyNjUwNSw4LjIyMi4yMDguMTQ2&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="94148528"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94148528"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94148528; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94148528]").text(description); $(".js-view-count[data-work-id=94148528]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94148528; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94148528']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 94148528, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "df4b8114527ed6ca45eaf97fb67f80c4" } } $('.js-work-strip[data-work-id=94148528]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94148528,"title":"Decoder-based evolutionary algorithm for bi-objective just-in-time single-machine job-shop","translated_title":"","metadata":{"abstract":"The bi-objective just-in-time single-machine job-shop scheduling problem (JIT-JSP) aims at simultaneously minimizing earliness and tardiness. 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A comparison study with a classical multi-objective evolutionary algorithm underlines the performance of the proposed decoding strategy and the interest of the approximate decoder.","publisher":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)"},"translated_abstract":"The bi-objective just-in-time single-machine job-shop scheduling problem (JIT-JSP) aims at simultaneously minimizing earliness and tardiness. In this paper, a multi-objective decoder-based evolutionary algorithm is proposed. The decoding strategy divides the search into two steps. In the first step, the search of the permutation order of the jobs is realized thanks to a multi-objective evolutionary algorithm. For a fixed permutation, the decoder algorithm optimizes the multi-objective timing sub-problem in the second step. 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A comparison study with a classical multi-objective evolutionary algorithm underlines the performance of the proposed decoding strategy and the interest of the approximate decoder.","internal_url":"https://www.academia.edu/94148528/Decoder_based_evolutionary_algorithm_for_bi_objective_just_in_time_single_machine_job_shop","translated_internal_url":"","created_at":"2023-01-02T02:46:51.116-08:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":9335826,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[],"downloadable_attachments":[{"id":96686819,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/96686819/thumbnails/1.jpg","file_name":"SSCI16_paper_158.pdf","download_url":"https://www.academia.edu/attachments/96686819/download_file?st=MTczMjcyNjUwNSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Decoder_based_evolutionary_algorithm_for.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/96686819/SSCI16_paper_158-libre.pdf?1672656629=\u0026response-content-disposition=attachment%3B+filename%3DDecoder_based_evolutionary_algorithm_for.pdf\u0026Expires=1732730105\u0026Signature=LiWedd0P0DlpjTdyzMOM8V6FbMbuJnlhkjYv3jSY6F13Kx7zZQuHDURQh0pdSHqlbFf~v1m9vaYEd2EJr3D8ZB7URQTg1CfLaohMIZ3LEkCAEWNel-G9DA~58s4tFUDZAqnNhs-oOIdVFw5zf~gBessCOaeevu-Wwc~L8cZY~FaEwpvmbDzwpHFv6EhLhRMHpW9To66ihbZZE~drzmzt-qzIAEu2fp8gUCpIRZK7UDAUnNhXHSgenWVi4lx8WKX~cKphKOswtdmx4Rhmu41bKaPA3Gfup0Xg9~TZO2f4sGBwANJDRP9qyp7KI-kK0T2ba7lTEevjyUxvhcJ-ZQipmw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Decoder_based_evolutionary_algorithm_for_bi_objective_just_in_time_single_machine_job_shop","translated_slug":"","page_count":8,"language":"en","content_type":"Work","owner":{"id":9335826,"first_name":"Laetitia","middle_initials":null,"last_name":"Jourdan","page_name":"LJourdan","domain_name":"independent","created_at":"2014-02-20T04:28:06.355-08:00","display_name":"Laetitia Jourdan","url":"https://independent.academia.edu/LJourdan"},"attachments":[{"id":96686819,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/96686819/thumbnails/1.jpg","file_name":"SSCI16_paper_158.pdf","download_url":"https://www.academia.edu/attachments/96686819/download_file?st=MTczMjcyNjUwNSw4LjIyMi4yMDguMTQ2&","bulk_download_file_name":"Decoder_based_evolutionary_algorithm_for.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/96686819/SSCI16_paper_158-libre.pdf?1672656629=\u0026response-content-disposition=attachment%3B+filename%3DDecoder_based_evolutionary_algorithm_for.pdf\u0026Expires=1732730105\u0026Signature=LiWedd0P0DlpjTdyzMOM8V6FbMbuJnlhkjYv3jSY6F13Kx7zZQuHDURQh0pdSHqlbFf~v1m9vaYEd2EJr3D8ZB7URQTg1CfLaohMIZ3LEkCAEWNel-G9DA~58s4tFUDZAqnNhs-oOIdVFw5zf~gBessCOaeevu-Wwc~L8cZY~FaEwpvmbDzwpHFv6EhLhRMHpW9To66ihbZZE~drzmzt-qzIAEu2fp8gUCpIRZK7UDAUnNhXHSgenWVi4lx8WKX~cKphKOswtdmx4Rhmu41bKaPA3Gfup0Xg9~TZO2f4sGBwANJDRP9qyp7KI-kK0T2ba7lTEevjyUxvhcJ-ZQipmw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":26817,"name":"Algorithm","url":"https://www.academia.edu/Documents/in/Algorithm"},{"id":265625,"name":"Evolutionary Algorithm","url":"https://www.academia.edu/Documents/in/Evolutionary_Algorithm"},{"id":272592,"name":"Mathematical Optimization","url":"https://www.academia.edu/Documents/in/Mathematical_Optimization"},{"id":400356,"name":"Job shop scheduling","url":"https://www.academia.edu/Documents/in/Job_shop_scheduling"},{"id":484848,"name":"Tardiness","url":"https://www.academia.edu/Documents/in/Tardiness"}],"urls":[{"id":27654183,"url":"https://www.wikidata.org/entity/Q59262662"}]}, dispatcherData: dispatcherData }); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="94148524"><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/94148524/Automatic_Configuration_of_Multi_Objective_Local_Search_Algorithms_for_Permutation_Problems"><img alt="Research paper thumbnail of Automatic Configuration of Multi-Objective Local Search Algorithms for Permutation Problems" class="work-thumbnail" src="https://attachments.academia-assets.com/96686823/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/94148524/Automatic_Configuration_of_Multi_Objective_Local_Search_Algorithms_for_Permutation_Problems">Automatic Configuration of Multi-Objective Local Search Algorithms for Permutation Problems</a></div><div class="wp-workCard_item"><span>Evolutionary Computation</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Automatic algorithm configuration (AAC) is becoming a key ingredient in the design of high-perfor...</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">Automatic algorithm configuration (AAC) is becoming a key ingredient in the design of high-performance solvers for challenging optimisation problems. However, most existing work on AAC deals with configuration procedures that optimise a single performance metric of a given, single-objective algorithm. Of course, these configurators can also be used to optimise the performance of multi-objective algorithms, as measured by a single performance indicator. In this work, we demonstrate that better results can be obtained by using a native, multi-objective algorithm configuration procedure. Specifically, we compare three AAC approaches: one considering only the hypervolume indicator, a second optimising the weighted sum of hypervolume and spread, and a third that simultaneously optimises these complementary indicators, using a genuinely multi-objective approach. We assess these approaches by applying them to a highly-parametric local search framework for two widely studied multi-objective...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e7f7298f681a308ea97dc22b424b356c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96686823,"asset_id":94148524,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96686823/download_file?st=MTczMjcyNjUwNSw4LjIyMi4yMDguMTQ2&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="94148524"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94148524"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94148524; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94148524]").text(description); $(".js-view-count[data-work-id=94148524]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94148524; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94148524']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 94148524, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e7f7298f681a308ea97dc22b424b356c" } } $('.js-work-strip[data-work-id=94148524]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94148524,"title":"Automatic Configuration of Multi-Objective Local Search Algorithms for Permutation Problems","translated_title":"","metadata":{"abstract":"Automatic algorithm configuration (AAC) is becoming a key ingredient in the design of high-performance solvers for challenging optimisation problems. However, most existing work on AAC deals with configuration procedures that optimise a single performance metric of a given, single-objective algorithm. Of course, these configurators can also be used to optimise the performance of multi-objective algorithms, as measured by a single performance indicator. In this work, we demonstrate that better results can be obtained by using a native, multi-objective algorithm configuration procedure. Specifically, we compare three AAC approaches: one considering only the hypervolume indicator, a second optimising the weighted sum of hypervolume and spread, and a third that simultaneously optimises these complementary indicators, using a genuinely multi-objective approach. We assess these approaches by applying them to a highly-parametric local search framework for two widely studied multi-objective...","publisher":"MIT Press - Journals","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Evolutionary Computation"},"translated_abstract":"Automatic algorithm configuration (AAC) is becoming a key ingredient in the design of high-performance solvers for challenging optimisation problems. However, most existing work on AAC deals with configuration procedures that optimise a single performance metric of a given, single-objective algorithm. Of course, these configurators can also be used to optimise the performance of multi-objective algorithms, as measured by a single performance indicator. In this work, we demonstrate that better results can be obtained by using a native, multi-objective algorithm configuration procedure. 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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="94148523"><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/94148523/M%C3%A9taheuristiques_Coop%C3%A9ratives_du_d%C3%A9terministe_au_stochastique"><img alt="Research paper thumbnail of Métaheuristiques Coopératives : du déterministe au stochastique" class="work-thumbnail" src="https://attachments.academia-assets.com/96686828/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/94148523/M%C3%A9taheuristiques_Coop%C3%A9ratives_du_d%C3%A9terministe_au_stochastique">Métaheuristiques Coopératives : du déterministe au stochastique</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Ce travail présente nos principales contributions à la résolution de problèmes d&#39;optimisation...</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">Ce travail présente nos principales contributions à la résolution de problèmes d&#39;optimisation combinatoire en environnements déterministe et stochastique. Au niveau des métaheuristiques, une vue unifiée de la conception de métaheuristiques à solution unique et de métaheuristiques multi-objective est proposée. Cette unification a permis notamment de retravailler la plateforme ParadisEO afin d&#39;offrir plus de flexibilité et de polyvalence. La synthèse des travaux présente également une vue unifiée des métaheuristiques coopératives. Nous montrons que cette vue convient aussi bien pour des coopérations entre métaheuristiques que des coopération entre des métaheuristiques et des méthodes exactes mais également des coopérations entre des métaheuristiques et des algorithmes d&#39;extraction de connaissances. Différents exemples de coopérations réalisées dans mes travaux de recherche illustent ces coopérations et leur application à des problèmes d&#39;optimisation combinatoire mono- ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4300c6aed03fd83fa28b43dd2266a9ce" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96686828,"asset_id":94148523,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96686828/download_file?st=MTczMjcyNjUwNSw4LjIyMi4yMDguMTQ2&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="94148523"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94148523"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94148523; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94148523]").text(description); $(".js-view-count[data-work-id=94148523]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94148523; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94148523']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 94148523, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4300c6aed03fd83fa28b43dd2266a9ce" } } $('.js-work-strip[data-work-id=94148523]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94148523,"title":"Métaheuristiques Coopératives : du déterministe au stochastique","translated_title":"","metadata":{"abstract":"Ce travail présente nos principales contributions à la résolution de problèmes d\u0026#39;optimisation combinatoire en environnements déterministe et stochastique. 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thumbnail of MOCA-I: Discovering Rules and Guiding Decision Maker in the Context of Partial Classification in Large and Imbalanced Datasets" class="work-thumbnail" src="https://attachments.academia-assets.com/96686814/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/94148521/MOCA_I_Discovering_Rules_and_Guiding_Decision_Maker_in_the_Context_of_Partial_Classification_in_Large_and_Imbalanced_Datasets">MOCA-I: Discovering Rules and Guiding Decision Maker in the Context of Partial Classification in Large and Imbalanced Datasets</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3f6bc6f7548cd0f130d9967916a25bd9" 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In this paper, a multi-objective decoder-based evolutionary algorithm is proposed. The decoding strategy divides the search into two steps. In the first step, the search of the permutation order of the jobs is realized thanks to a multi-objective evolutionary algorithm. For a fixed permutation, the decoder algorithm optimizes the multi-objective timing sub-problem in the second step. Thus each permutation order induces a Pareto set of solutions. Two different decoding strategies to fix the idle times are proposed, one approximate and one exact. A comparison study with a classical multi-objective evolutionary algorithm underlines the performance of the proposed decoding strategy and the interest of the approximate decoder.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="df4b8114527ed6ca45eaf97fb67f80c4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96686819,"asset_id":94148528,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96686819/download_file?st=MTczMjcyNjUwNSw4LjIyMi4yMDguMTQ2&st=MTczMjcyNjUwNSw4LjIyMi4yMDguMTQ2&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="94148528"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94148528"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94148528; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94148528]").text(description); $(".js-view-count[data-work-id=94148528]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94148528; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94148528']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 94148528, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "df4b8114527ed6ca45eaf97fb67f80c4" } } $('.js-work-strip[data-work-id=94148528]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94148528,"title":"Decoder-based evolutionary algorithm for bi-objective just-in-time single-machine job-shop","translated_title":"","metadata":{"abstract":"The bi-objective just-in-time single-machine job-shop scheduling problem (JIT-JSP) aims at simultaneously minimizing earliness and tardiness. 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A comparison study with a classical multi-objective evolutionary algorithm underlines the performance of the proposed decoding strategy and the interest of the approximate decoder.","publisher":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)"},"translated_abstract":"The bi-objective just-in-time single-machine job-shop scheduling problem (JIT-JSP) aims at simultaneously minimizing earliness and tardiness. In this paper, a multi-objective decoder-based evolutionary algorithm is proposed. The decoding strategy divides the search into two steps. In the first step, the search of the permutation order of the jobs is realized thanks to a multi-objective evolutionary algorithm. For a fixed permutation, the decoder algorithm optimizes the multi-objective timing sub-problem in the second step. 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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="94148524"><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/94148524/Automatic_Configuration_of_Multi_Objective_Local_Search_Algorithms_for_Permutation_Problems"><img alt="Research paper thumbnail of Automatic Configuration of Multi-Objective Local Search Algorithms for Permutation Problems" class="work-thumbnail" src="https://attachments.academia-assets.com/96686823/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/94148524/Automatic_Configuration_of_Multi_Objective_Local_Search_Algorithms_for_Permutation_Problems">Automatic Configuration of Multi-Objective Local Search Algorithms for Permutation Problems</a></div><div class="wp-workCard_item"><span>Evolutionary Computation</span><span>, 2018</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Automatic algorithm configuration (AAC) is becoming a key ingredient in the design of high-perfor...</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">Automatic algorithm configuration (AAC) is becoming a key ingredient in the design of high-performance solvers for challenging optimisation problems. However, most existing work on AAC deals with configuration procedures that optimise a single performance metric of a given, single-objective algorithm. Of course, these configurators can also be used to optimise the performance of multi-objective algorithms, as measured by a single performance indicator. In this work, we demonstrate that better results can be obtained by using a native, multi-objective algorithm configuration procedure. Specifically, we compare three AAC approaches: one considering only the hypervolume indicator, a second optimising the weighted sum of hypervolume and spread, and a third that simultaneously optimises these complementary indicators, using a genuinely multi-objective approach. 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We assess these approaches by applying them to a highly-parametric local search framework for two widely studied multi-objective...","publisher":"MIT Press - Journals","publication_date":{"day":null,"month":null,"year":2018,"errors":{}},"publication_name":"Evolutionary Computation"},"translated_abstract":"Automatic algorithm configuration (AAC) is becoming a key ingredient in the design of high-performance solvers for challenging optimisation problems. However, most existing work on AAC deals with configuration procedures that optimise a single performance metric of a given, single-objective algorithm. Of course, these configurators can also be used to optimise the performance of multi-objective algorithms, as measured by a single performance indicator. In this work, we demonstrate that better results can be obtained by using a native, multi-objective algorithm configuration procedure. 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Au niveau des métaheuristiques, une vue unifiée de la conception de métaheuristiques à solution unique et de métaheuristiques multi-objective est proposée. Cette unification a permis notamment de retravailler la plateforme ParadisEO afin d&#39;offrir plus de flexibilité et de polyvalence. La synthèse des travaux présente également une vue unifiée des métaheuristiques coopératives. Nous montrons que cette vue convient aussi bien pour des coopérations entre métaheuristiques que des coopération entre des métaheuristiques et des méthodes exactes mais également des coopérations entre des métaheuristiques et des algorithmes d&#39;extraction de connaissances. Différents exemples de coopérations réalisées dans mes travaux de recherche illustent ces coopérations et leur application à des problèmes d&#39;optimisation combinatoire mono- ...</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4300c6aed03fd83fa28b43dd2266a9ce" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":96686828,"asset_id":94148523,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/96686828/download_file?st=MTczMjcyNjUwNSw4LjIyMi4yMDguMTQ2&st=MTczMjcyNjUwNSw4LjIyMi4yMDguMTQ2&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="94148523"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span><span id="work-strip-rankings-button-container"></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="94148523"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 94148523; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=94148523]").text(description); $(".js-view-count[data-work-id=94148523]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 94148523; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='94148523']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span><span><script>$(function() { new Works.PaperRankView({ workId: 94148523, container: "", }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-f77ea15d77ce96025a6048a514272ad8becbad23c641fc2b3bd6e24ca6ff1932.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4300c6aed03fd83fa28b43dd2266a9ce" } } $('.js-work-strip[data-work-id=94148523]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":94148523,"title":"Métaheuristiques Coopératives : du déterministe au stochastique","translated_title":"","metadata":{"abstract":"Ce travail présente nos principales contributions à la résolution de problèmes d\u0026#39;optimisation combinatoire en environnements déterministe et stochastique. 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thumbnail of MOCA-I: Discovering Rules and Guiding Decision Maker in the Context of Partial Classification in Large and Imbalanced Datasets" class="work-thumbnail" src="https://attachments.academia-assets.com/96686814/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/94148521/MOCA_I_Discovering_Rules_and_Guiding_Decision_Maker_in_the_Context_of_Partial_Classification_in_Large_and_Imbalanced_Datasets">MOCA-I: Discovering Rules and Guiding Decision Maker in the Context of Partial Classification in Large and Imbalanced Datasets</a></div><div class="wp-workCard_item"><span>Lecture Notes in Computer Science</span><span>, 2013</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3f6bc6f7548cd0f130d9967916a25bd9" 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