Marie-Eléonore Kessaci
Publications
Publications
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The Parameterized Complexity of Local Search for MO-TSP26ème édition du congrès annuel de la Société Française de Recherche Opérationnelle et d’Aide à la Décision, Feb 2025, Champs-sur-Marne, France |
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The Parameterized Complexity of Local Search for Multi-objective TSPEuropean Symposium on Algorithms (Submitted), Sep 2025, Varsovie, Poland |
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MH-Builder: a C++ framework for designing adaptive metaheuristics for single and multi-objective optimization26ème édition du congrès annuel de la Société Française de Recherche Opérationnelle et d'Aide à la Décision, ROADEF, Feb 2025, Champs-sur-Marne, France |
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Generalizing the Structure of a University Timetabling Solver for Flexible Automatic Algorithm Configuration2025 Genetic and Evolutionary Computation Conference (GECCO 2025), ACM SIGEVO, Jul 2025, Málaga, Spain. pp.555-558, ⟨10.1145/3712255.3726683⟩ |
The Parameterized Complexity of Local Search for MOTSP26e Journées Graphes et Algorithmes, Nov 2024, Dijon, France |
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Solution-based Knowledge Discovery for Multi-objective OptimizationPPSN 2024, Sep 2024, Hagenberg, Austria |
Investigation of the Benefit of Extracting Patterns from Local Optima to Solve a Bi-objective VRPTWMIC 2024 - Metaheuristics International Conference, Jun 2024, Lorient, France. pp.62-77, ⟨10.1007/978-3-031-62912-9_7⟩ |
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Improving the Relevance of Artificial Instances for Curriculum-Based Course Timetabling through Feasibility PredictionGECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation, Jul 2023, Lisbon Portugal, France. pp.203-206, ⟨10.1145/3583133.3590690⟩ |
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New Neighborhood Strategies for the Bi-objective Vehicle Routing Problem with Time WindowsMIC 2022 - Metaheuristics International Conference, Jul 2022, Ortigia-Syracuse, Italy. pp.45-60, ⟨10.1007/978-3-031-26504-4_4⟩ |
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Integration of Knowledge Discovery into MOEA/DROADEF 2023, Feb 2023, Rennes, France |
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When Simpler is Better: Automated Configuration of a University Timetabling SolverIEEE 2023 Congress on Evolutionary Computation, IEEE, Jul 2023, Chicago, United States. pp.01-08, ⟨10.1109/CEC53210.2023.10253986⟩ |
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Predicting Feasibility in University TimetablingROADEF 2023, Société Française de Recherche Opérationnelle et d'Aide à la Décision, Feb 2023, Rennes, France |
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Analysis of Search Landscape Samplers for Solver Performance Prediction on a University Timetabling ProblemParallel Problem Solving from Nature – PPSN XVII, Sep 2022, Dortmund, Germany. pp.548-561, ⟨10.1007/978-3-031-14714-2_38⟩ |
A Novel Multi-objective Decomposition Formulation for Per-Instance ConfigurationBRACIS, Nov 2022, Campinas, Brazil. pp.325-339, ⟨10.1007/978-3-031-21686-2_23⟩ |
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Landscape-based Performance Prediction for University Timetabling Optimization23ème congrès annuel de la Société Française de Recherche Opérationnelle et d'Aide à la Décision, INSA Lyon, Feb 2022, Villeurbanne - Lyon, France |
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Machine Learning for Multi-Objective ProblemsROADEF 2022 - 23ème congrès annuel de la Société Française de Recherche Opérationnelle et d'Aide à la Décision, INSA Lyon, Feb 2022, Villeurbanne - Lyon, France |
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Exploiting landscape features for fitness prediction in university timetablingGECCO ’22 Companion: Companion Conference on Genetic and Evolutionary Computation, Jul 2022, Boston, MA, United States. pp.192-195, ⟨10.1145/3520304.3528910⟩ |
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Enhancing MOEA/D with Learning: Application to Routing Problems with Time WindowsGECCO 2022 - The Genetic and Evolutionary Computation Conference, Jul 2022, Boston, United States. ⟨10.1145/3520304.3528909⟩ |
Dynamic Learning in Hyper-Heuristics to Solve Flowshop ProblemsBRACIS, Nov 2021, online, Brazil. pp.155-169, ⟨10.1007/978-3-030-91702-9_11⟩ |
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Flowshop NEH-Based Heuristic RecommendationEuropean Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP), Apr 2021, Seville, Spain. pp.136-151, ⟨10.1007/978-3-030-72904-2_9⟩ |
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AutoTSC: Optimization Algorithm to Automatically Solve the Time Series Classification ProblemICTAI 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence, Nov 2021, Washington, United States |
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Using Machine Learning to Enhance Clarke and Wright HeuristicConférence ROADEF 2021, Apr 2021, Mulhouse, France |
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Investigating the landscape of a hybrid local search approach for a timetabling problemGECCO '21 Companion: Companion Conference on Genetic and Evolutionary Computation, ACM, Jul 2021, Lille, France. pp.1665-1673, ⟨10.1145/3449726.3463175⟩ |
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Automatic Algorithm Multi-Configuration Applied to an Optimization Algorithm21st International Conference on Hybrid Intelligent Systems (HIS 2021), Dec 2021, online, United States. ⟨10.1007/978-3-030-96305-7_15⟩ |
Local Optima Network Sampling for Permutation Flowshop2021 IEEE Congress on Evolutionary Computation (CEC), Jun 2021, Kraków, Poland. pp.1131-1138, ⟨10.1109/CEC45853.2021.9504690⟩ |
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Time-Dependent Automatic Parameter Configuration of a Local Search AlgorithmGECCO '20 Companion: Companion Conference on Genetic and Evolutionary Computation, ACM, Jul 2020, Cancún, Mexico. ⟨10.1145/3377929.3398107⟩ |
Impact of the Discretization of VOCs for Cancer Prediction Using a Multi-Objective AlgorithmLION 2020 - Learning and Intelligent Optimization, 2020, Athens, Greece. pp.151-157, ⟨10.1007/978-3-030-53552-0_16⟩ |
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Automatic Configuration of a Multi-objective Local Search for Imbalanced ClassificationPPSN 2020, Sep 2020, Leiden, Netherlands. pp.65-77, ⟨10.1007/978-3-030-58112-1_5⟩ |
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Optimisation multiobjectif pour le diagnostic de pathologies via biomarqueursCongrès annuel de la société Française de Recherche Opérationnelle et d’Aide à la Décision (ROADEF), Feb 2020, Montpellier, France |
Multi-objective Automatic Algorithm Configuration for the Classification Problem of Imbalanced Data2020 IEEE Congress on Evolutionary Computation (CEC), Jul 2020, Glasgow, United Kingdom. pp.1-8, ⟨10.1109/CEC48606.2020.9185785⟩ |
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A Dynamic Algorithm Framework to Automatically Design a Multi-Objective Local SearchROADEF 2019, Feb 2019, Le Havre, France |
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Automatic Configuration of a Dynamic Hill Climbing AlgorithmSLS - International Workshop on Stochastic Local Search Algorithms, Sep 2019, Villeneuve d'Ascq, France |
TPOT-SH: a FasterOptimization Algorithm to Solve the AutoML Problem on Large DatasetsICTAI - International Conference on Tools with Artificial Intelligence, Nov 2019, Portland, United States |
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Configuration of a Dynamic MOLS Algorithm for Bi-objective Flowshop SchedulingEvolutionary Multi-Criterion Optimization, Mar 2019, Lansing, United States. pp.565-577 |
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TPOT-SH: a Faster Optimization Algorithm to Solve the AutoML Problem on Large DatasetsSLS - International Workshop on Stochastic Local Search Algorithms, Sep 2019, Villeneuve d'Ascq, France |
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Meta-learning on flowshop using fitness landscape analysisthe Genetic and Evolutionary Computation Conference, Jul 2019, Prague, Czech Republic. pp.925-933 |
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New Initialisation Techniques for Multi-objective Local SearchParallel Problem Solving from Nature - PPSN XV, Sep 2018, Coimbra, Portugal. ⟨10.1007/978-3-319-99253-2_26⟩ |
Automatic Configuration of Multi-objective Optimization Algorithms. Impact of Correlation between Objectives.30th International Conference on Tools with Artificial Intelligence, Oct 2018, Volos, Greece |
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Adaptive Multi-Objective Local Search Algorithms for the Permutation Flowshop Scheduling ProblemLearning and Intelligent Optimization Conference (LION 12), Jun 2018, Kalamata, Greece |
Recommending Meta-Heuristics and Configurations for the Flowshop Problem via Meta-Learning: Analysis and Design2018 7th Brazilian Conference on Intelligent Systems (BRACIS), Oct 2018, Sao Paulo, Brazil. pp.163-168 |
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Meta-Learning for Optimization: A Case Study on the Flowshop Problem Using Decision Trees2018 IEEE Congress on Evolutionary Computation (CEC), Jul 2018, Rio de Janeiro, France. ⟨10.1109/CEC.2018.8477664⟩ |
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Automatically Configuring Multi-objective Local Search Using Multi-objective OptimisationEMO 2017 - 9th International Conference on Evolutionary Multi-Criterion Optimization, Mar 2017, Münster, Germany. pp.61-73, ⟨10.1007/978-3-319-54157-0_5⟩ |
Neutral Neighbors in Bi-objective Optimization: Distribution of the Most Promising for Permutation ProblemsEMO 2017 - 9th International Conference on Evolutionary Multi-Criterion Optimization, Mar 2017, Munster, Germany. pp.344-358, ⟨10.1007/978-3-319-54157-0_24⟩ |
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AMH: une plate-forme pour le design et le contrôle automatique de métaheuristiques multi-objectifROADEF2017: 18ème Conférence ROADEF de la Société Française de Recherche Opérationnelle et d'Aide à la Décision, Feb 2017, Metz, France |
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Automatic Design of Multi-Objective Local Search AlgorithmsGECCO 2017 - Genetic and Evolutionary Computation Conference, Jul 2017, Berlin, Germany. pp.227-234, ⟨10.1145/3071178.3071323⟩ |
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AMH: a new Framework to Design Adaptive Metaheuristics12th Metaheuristics International Conference, Jul 2017, Barcelona, Spain |
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A new constructive heuristic for the No-Wait Flowshop Scheduling Problem11th Learning and Intelligent OptimizatioN Conference, Jun 2017, Nizhny Novgorod, Russia |
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De nouvelles meilleures solutions pour le problème d'ordonnancement No-Wait FlowshopROADEF2017: 18ème Conférence ROADEF de la Société Française de Recherche Opérationnelle et d'Aide à la Décision, Feb 2017, Metz, France |
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An Iterated Greedy-based Approach Exploiting Promising Sub-Sequences of Jobs to solve the No-Wait Flowshop Scheduling ProblemMIC 2017 - 12th Metaheuristics International Conference, Jul 2017, Barcelona, Spain |
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MO-ParamILS: A Multi-objective Automatic Algorithm Configuration FrameworkLearning and Intelligent Optimization, May 2016, Ischia, Italy. pp.32-47, ⟨10.1007/978-3-319-50349-3_3⟩ |
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Feature Selection using Tabu Search with Learning Memory: Learning Tabu SearchLearning and Intelligent OptimizatioN Conference LION 10, May 2016, Ischia Island (Napoli), Italy |
Multi-objective Neutral Neighbors? What could be the definition(s)?Genetic and Evolutionary Computation Conference, 2016, Denver, CO, United States. pp.349--356, ⟨10.1145/2908812.2908902⟩ |
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Neutral but a Winner! How Neutrality Helps Multiobjective Local Search AlgorithmsEvolutionary Multi-Criterion Optimization - 8th International Conference, EMO 2015, Guimarães, Portugal, March 29 -April 1, 2015. Proceedings, Part I, 2015, Guimarães, Portugal. pp.34--47, ⟨10.1007/978-3-319-15934-8_3⟩ |
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Fitness Landscape of the Factoradic Representation on the Permutation Flowshop Scheduling ProblemLION 9 - 9th International Conference on Learning and Intelligent OptimizatioN, Jan 2015, Lille, France. pp.151-164, ⟨10.1007/978-3-319-19084-6_14⟩ |
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Algorithm comparison by automatically configurable stochastic local search frameworks: a case study using flow-shop scheduling problemsHM 2014 - 9th International Workshop Hybrid Metaheuristics , Jun 2014, Hamburg, Germany. pp.30-44, ⟨10.1007/978-3-319-07644-7_3⟩ |
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A Template for Designing Single-Solution Hybrid MetaheuristicsGenetic and evolutionary computation companion, GECCO Comp'14, 2014, Vancouver, Canada |
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Towards the Automatic Design of MetaheuristicsMIC 2013 - 10th Metaheuristics International Conference, Aug 2013, Singapore, Singapore. pp.1-3 |
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Automatic Design of a Hybrid Iterated Local Search for the Multi-Mode Resource-Constrained Multi-Project Scheduling ProblemMISTA 2013 - Multidisciplinary International Conference on Scheduling: Theory and Applications, Aug 2013, Gent, Belgium. pp.1-6 |
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Automatic Design of Hybrid Stochastic Local Search MetaheuristicsHM 2013 - 8th International Workshop on Hybrid Metaheuristics, May 2013, Ischia, Italy. pp.144-158, ⟨10.1007/978-3-642-38516-2_12⟩ |
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Neutralité du problème de coloration de grapheROADEF 2013 : 14e congrès de la Société Française de Recherche Opérationnelle et d'Aide à la Décision, Feb 2013, Troyes, France |
Neutrality in the Graph Coloring ProblemLearning and Intelligent OptimizatioN Conference, Jan 2013, Catania, Italy. pp.125--130 |
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Conception de recherche locale en présence de neutralitéROADEF 2012 : 13e congrès de la Société Française de Recherche Opérationnelle et d'Aide à la Décision, Apr 2012, Angers, France |
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NILS: a Neutrality-based Iterated Local Search and its application to Flowshop Scheduling11th European Conference on Evolutionary Computation in Combinatorial Optimisation, Apr 2011, Turino, Italy. pp.191--202 |
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On the Neutrality of Flowshop Scheduling Fitness LandscapesLearning and Intelligent OptimizatioN Conference (LION 5), Jan 2011, Rome, Italy. pp.238--252, ⟨10.1007/978-3-642-25566-3_18⟩ |
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The Road to VEGAS: Guiding the Search over Neutral NetworksGenetic And Evolutionary Computation Conference, Jun 2011, Dublin, Ireland. pp.1979--1986, ⟨10.1145/2001576.2001842⟩ |
A Fitness Landscape Analysis for the Permutation Flowshop Scheduling ProblemInternational Conference on Metaheuristics and Nature Inspired Computing (META 2010), Oct 2010, Djerba, Tunisia |
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Une nouvelle mesure de distance pour l'ACVRPROADEF 2010 : 11e congrès annuel de la Société Française de Recherche Opérationnelle et d'Aide à la Décision, Feb 2010, Toulouse, France |
Comparison of Neighborhood for the HFF-AVRPIEEE International Conference on Computer Systems and Applications (AICCSA 2010 workshop), May 2010, Hammamet, Tunisia |
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Special Cluster on Stochastic Local Search: Recent Developments and TrendsInternational Transactions in Operational Research, 29 (5), pp.2731-3232, 2022, ⟨10.1111/itor.13107⟩ |
Learning and Intelligent Optimization - 9th International Conference, LION 9, Lille, France, January 12-15, 2015. Revised Selected PapersClarisse Dhaenens and Laetitia Jourdan and Marie-Eléonore Marmion. Springer, 8994, 2015, Lecture Notes in Computer Science, 978-3-319-19083-9. ⟨10.1007/978-3-319-19084-6⟩ |
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Neutrality in the Graph Coloring Problem[Research Report] RR-8215, INRIA. 2013, pp.15 |
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A new distance measure based on the exchange operator for the HFF-AVRP[Intern report] RR-7263, INRIA. 2010 |
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Recherche locale et optimisation combinatoire : de l'analyse structurelle d'un problème à la conception d'algorithmes efficacesMathématique discrète [cs.DM]. Université des Sciences et Technologie de Lille - Lille I, 2011. Français. ⟨NNT : ⟩ |
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Knowledge-based Design of Stochastic Local Search Algorithms in Combinatorial OptimizationDiscrete Mathematics [cs.DM]. Université de Lille, 2019 |
Mary-MorstanLogiciel hal-04444425v1 |
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MH-BuilderLogiciel hal-04444623v1 |