Carola Doerr
- Centre National de la Recherche Scientifique (CNRS)
- Recherche Opérationnelle (RO)
- Sorbonne Université (SU)
Publications
Publications
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Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm SelectionThe 18th Learning and Intelligent OptimizatioN Conference (LION 2024), Jun 2024, Ischia, Italy. pp.361-376, ⟨10.1007/978-3-031-75623-8_29⟩ |
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Geometric Learning in Black-Box Optimization: A GNN Framework for Algorithm Performance PredictionProc. of the Genetic and Evolutionary Computation Conference (GECCO 2025, Companion material), ACM, Jul 2025, Malaga, Spain. ⟨10.1145/3712255.3726696⟩ |
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MO-IOHinspector: Anytime Benchmarking of Multi-Objective Algorithms using IOHprofilerEMO 2025, 13th International Conference on Evolutionary Multi-Criterion Optimization, Mar 2025, Canberra, Australia. pp.242-256, ⟨10.1007/978-981-96-3506-1_17⟩ |
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Multi-parameter Control for the (1+(L,L))-GA on OneMax via Deep Reinforcement LearningFoundations of Genetic Algorithms XVIII (FOGA), ACM/SIGEVO, 2025, Leiden, Netherlands. ⟨10.1145/3729878.3746703⟩ |
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Analyzing Single-objective Black-box Optimization Algorithms Using the Empirical Attainment FunctionGenetic and Evolutionary Computation Conference, Association for Computing Machinery, Jul 2025, Malaga, Spain. ⟨10.1145/3712255.3734242⟩ |
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Cascading CMA-ES Instances for Generating Input-diverse Solution BatchesProc. of the Genetic and Evolutionary Computation Conference (GECCO, Companion material, BBOB workshop), ACM, Jul 2025, Malaga, Spain. ⟨10.1145/3712255.3734304⟩ |
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On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+(L,L))-GAGECCO '25: Genetic and Evolutionary Computation Conference, Jul 2025, Malaga, Spain. pp.1162-1171, ⟨10.1145/3712256.3726395⟩ |
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Enhancing Parameter Control Policies with State Information18th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA), Aug 2025, Leiden, Netherlands. ⟨10.1145/3729878.3746633⟩ |
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Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks2024 IEEE Congress on Evolutionary Computation (CEC), IEEE, Jun 2024, Yokohama, Japan. pp.1-8, ⟨10.1109/CEC60901.2024.10611779⟩ |
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Large-scale Benchmarking of Metaphor-based Optimization HeuristicsGECCO '24: Proceedings of the Genetic and Evolutionary Computation Conference, Jul 2024, Melbourne, Australia. pp.41 - 49, ⟨10.1145/3638529.3654122⟩ |
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Empirical Analysis of the Dynamic Binary Value Problem with IOHprofiler18th International Conference on Parallel Problem Solving from Nature – PPSN XVIII, Sep 2024, Hagenberg, Austria. pp.20-35, ⟨10.1007/978-3-031-70068-2_2⟩ |
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Learned Features vs. Classical ELA on Affine BBOB FunctionsParallel Problem Solving from Nature – PPSN XVIII (PPSN 2024), Sep 2024, Hagenberg, Austria. pp.137-153, ⟨10.1007/978-3-031-70068-2_9⟩ |
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Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks2024 IEEE Congress on Evolutionary Computation (CEC), Jun 2024, Yokohama, Japan. pp.1-8, ⟨10.1109/CEC60901.2024.10611952⟩ |
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Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box OptimizationGECCO '24: Proceedings of the Genetic and Evolutionary Computation Conference, ACM, Jul 2024, Melbourne, Australia. pp.1007 - 1016, ⟨10.1145/3638529.3654100⟩ |
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Hybridizing Target-and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box OptimizationParallel Problem Solving from Nature – PPSN XVIII (PPSN 2024), Sep 2024, Hagenberg, Austria. pp.154-169, ⟨10.1007/978-3-031-70068-2_10⟩ |
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Selecting Pre-trained Models for Transfer Learning with Data-centric Meta-featuresAutoML Conference 2024 (Workshop Track), Sep 2024, Paris, France |
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Explainable Model-specific Algorithm Selection for Multi-Label Classification2022 IEEE Symposium Series on Computational Intelligence (SSCI), Dec 2022, Singapore, Singapore. pp.39-46, ⟨10.1109/SSCI51031.2022.10022177⟩ |
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MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization ContextsInternational Conference on Automated Machine Learning (AutoML 2023), Sep 2023, Potsdam, Germany |
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Self-Adjusting Weighted Expected Improvement for Bayesian OptimizationInternational Conference on Automated Machine Learning (AutoML 2023), Sep 2023, Potsdam, Germany |
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Comparison of Bayesian Optimization Algorithms for BBOB Problems in Dimensions 10 and 60GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation, ACM, Jul 2023, Lisbon, Portugal. pp.2390-2393, ⟨10.1145/3583133.3596314⟩ |
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Sensitivity Analysis of RF+clust for Leave-One-Problem-Out Performance Prediction2023 IEEE Congress on Evolutionary Computation (CEC), Jul 2023, Chicago, IL, United States. pp.1-8, ⟨10.1109/CEC53210.2023.10254146⟩ |
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Using Affine Combinations of BBOB Problems for Performance AssessmentGECCO '23: Genetic and Evolutionary Computation Conference, Jul 2023, Lisbon, Portugal. pp.873-881, ⟨10.1145/3583131.3590412⟩ |
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Using Automated Algorithm Configuration for Parameter ControlFOGA '23: Foundations of Genetic Algorithms XVII, Aug 2023, Potsdam, Germany. pp.38-49, ⟨10.1145/3594805.3607127⟩ |
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RF+clust for Leave-One-Problem-Out Performance PredictionApplications of Evolutionary Computation (EvoApplications 2023), Apr 2023, Brno, Czech Republic. pp.285-301, ⟨10.1007/978-3-031-30229-9_19⟩ |
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Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem InstancesGECCO '23: Genetic and Evolutionary Computation Conference, Jul 2023, Lisbon, Portugal. pp.529-537, ⟨10.1145/3583131.3590424⟩ |
Benchmarking and analyzing iterative optimization heuristics with IOHprofilerGECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation, Jul 2023, Lisbon, Portugal. pp.938-945, ⟨10.1145/3583133.3595057⟩ |
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Tight Runtime Bounds for Static Unary Unbiased Evolutionary Algorithms on Linear FunctionsGECCO '23: Genetic and Evolutionary Computation Conference, Jul 2023, Lisbon, Portugal. pp.1565-1574, ⟨10.1145/3583131.3590482⟩ |
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PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box OptimizationInternational Conference on Automated Machine Learning (AutoML 2023), Sep 2023, Potsdam, Germany |
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Computing Star Discrepancies with Numerical Black-Box Optimization AlgorithmsGECCO '23: Genetic and Evolutionary Computation Conference, Jul 2023, Lisbon, Portugal. pp.1330-1338, ⟨10.1145/3583131.3590456⟩ |
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Using Knowledge Graphs for Performance Prediction of Modular Optimization AlgorithmsApplications of Evolutionary Computation (EvoApplications 2023), Apr 2023, Brno, Czech Republic. pp.253-268, ⟨10.1007/978-3-031-30229-9_17⟩ |
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DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization ProblemsGECCO '23: Genetic and Evolutionary Computation Conference, Jul 2023, Lisbon, Portugal. pp.813-821, ⟨10.1145/3583131.3590401⟩ |
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Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis6th Workshop on Meta-Learning at NeurIPS 2022, Dec 2022, New Orleans, United States |
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The Importance of Landscape Features for Performance Prediction of Modular CMA-ES VariantsGECCO '22: Genetic and Evolutionary Computation Conference, Jul 2022, Boston, United States. ⟨10.1145/3512290.3528832⟩ |
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Per-run Algorithm Selection with Warm-starting using Trajectory-based Features17th Proceedings of Parallel Problem Solving from Nature - (PPSN) 2022, 2022, Dortmund, Germany. pp.46-60, ⟨10.1007/978-3-031-14714-2_4⟩ |
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Non-Elitist Selection Can Improve the Performance of Irace17th Proceedings of Parallel Problem Solving from Nature - (PPSN) 2022, Sep 2022, Dortmund, Germany. pp.32-45, ⟨10.1007/978-3-031-14714-2_3⟩ |
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High Dimensional Bayesian Optimization with Kernel Principal Component Analysis17th Proceedings of Parallel Problem Solving from Nature - (PPSN) 2022, Sep 2022, Dortmund, Germany. pp.118-131, ⟨10.1007/978-3-031-14714-2_9⟩ |
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Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm ConfigurationGECCO '22: Genetic and Evolutionary Computation Conference, Jul 2022, Boston, United States. pp.766--775, ⟨10.1145/3512290.3528846⟩ |
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Fast Re-Optimization of LeadingOnes with Frequent Changes2022 IEEE Congress on Evolutionary Computation (CEC), Jul 2022, Padua, Italy. pp.1-8, ⟨10.1109/CEC55065.2022.9870400⟩ |
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Trajectory-based Algorithm Selection with Warm-starting2022 IEEE Congress on Evolutionary Computation (CEC), Jul 2022, Padua, Italy. pp.1-8, ⟨10.1109/CEC55065.2022.9870222⟩ |
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SELECTOR: Selecting a Representative Benchmark Suite for Reproducible Statistical ComparisonGECCO '22: Genetic and Evolutionary Computation Conference, Jul 2022, Boston, United States. ⟨10.1145/3512290.3528809⟩ |
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Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration17th Proceedings of Parallel Problem Solving from Nature - (PPSN) 2022, Sep 2022, Dortmund, Germany. pp.18-31, ⟨10.1007/978-3-031-14714-2_2⟩ |
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PI is back! Switching Acquisition Functions in Bayesian Optimization2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, Dec 2022, New Orleans, United States |
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Analyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary AlgorithmsGECCO '22: Genetic and Evolutionary Computation Conference, Jul 2022, Boston, United States. ⟨10.1145/3512290.3528799⟩ |
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Automated Algorithm Selection for Radar Network ConfigurationGECCO '22: Genetic and Evolutionary Computation Conference, Jul 2022, Boston, United States. ⟨10.1145/3512290.3528825⟩ |
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Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB FunctionsApplications of Evolutionary Computation (EvoApplications 2021), Apr 2021, Sevilla (Virtual), Spain. pp.17-33, ⟨10.1007/978-3-030-72699-7_2⟩ |
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Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary AlgorithmsIEEE Congress on Evolutionary Computation (CEC'21), Jun 2021, Krakow, Poland |
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Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm SelectionGenetic and Evolutionary Computation Conference (GECCO 2021, Companion Material), Jul 2021, Lille, France. ⟨10.1145/3449726.3459578⟩ |
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MATE: A Model-based Algorithm Tuning Engine - A proof of concept towards transparent feature-dependent parameter tuning using symbolic regressionEvolutionary Computation in Combinatorial Optimization (EvoCOP'21), Apr 2021, Sevilla (on line), Spain. pp.51-67, ⟨10.1007/978-3-030-72904-2_4⟩ |
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Optimal Static Mutation Strength Distributions for the (1 + λ) Evolutionary Algorithm on OneMaxGenetic and Evolutionary Computation Conference (GECCO 2021), Jul 2021, Lille, France. pp.660-668, ⟨10.1145/3449639.3459389⟩ |
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Personalizing Performance Regression Models to Black-Box Optimization ProblemsGenetic and Evolutionary Computation Conference (GECCO 2021), Jul 2021, Lille, France. ⟨10.1145/3449639.3459407⟩ |
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Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic ModulesGenetic and Evolutionary Computation Conference (GECCO 2021, Companion Material, Workshop), Jul 2021, Lille (en ligne), France. ⟨10.1145/3449726.3463167⟩ |
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OPTION: OPTImization Algorithm Benchmarking ONtologyGenetic and Evolutionary Computation Conference (GECCO 2021, Companion Material), Jul 2021, Lille (on line), France. ⟨10.1145/3449726.3459579⟩ |
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Towards Feature-Based Performance Regression Using Trajectory DataApplications of Evolutionary Computation (EvoApplications 2021), Apr 2021, Sevilla (on line), Spain. pp.601-617, ⟨10.1007/978-3-030-72699-7_38⟩ |
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The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm SelectionGenetic and Evolutionary Computation Conference (GECCO 2021), Jul 2021, Lille, France. pp.687-696, ⟨10.1145/3449639.3459406⟩ |
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Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking FrameworksGenetic and Evolutionary Computation Conference (GECCO 2021), Jul 2021, Lille, France. ⟨10.1145/3449726.3463155⟩ |
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Optimal Mutation Rates for the $(1+\lambda )$ EA on OneMaxParallel Problem Solving from Nature – PPSN XVI (PPSN 2020), Sep 2020, Leiden, Netherlands. pp.574-587, ⟨10.1007/978-3-030-58115-2_40⟩ |
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High Dimensional Bayesian Optimization Assisted by Principal Component AnalysisParallel Problem Solving from Nature – PPSN XVI (PPSN 2020), Sep 2020, Leiden, Netherlands. pp.169-183, ⟨10.1007/978-3-030-58112-1_12⟩ |
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Variance Reduction for Better Sampling in Continuous DomainsParallel Problem Solving from Nature – PPSN XVI (PPSN 2020), Sep 2020, Leiden, Netherlands. pp.154-168, ⟨10.1007/978-3-030-58112-1_11⟩ |
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Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes2020 IEEE Symposium Series on Computational Intelligence (SSCI), Dec 2020, Canberra, Australia. pp.775-782, ⟨10.1109/SSCI47803.2020.9308180⟩ |
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Mutation Rate Control in the (1 + λ) Evolutionary Algorithm with a Self-adjusting Lower BoundMathematical Optimization Theory and Operations Research (MOTOR 2020), Jul 2020, Novosibirsk, Russia. pp.305-319, ⟨10.1007/978-3-030-58657-7_25⟩ |
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Exploratory Landscape Analysis is Strongly Sensitive to the Sampling StrategyParallel Problem Solving from Nature – PPSN XVI (PPSN 2020), Sep 2020, Leiden, Netherlands. pp.139-153, ⟨10.1007/978-3-030-58115-2_10⟩ |
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Towards Dynamic Algorithm Selection for Numerical Black-Box Optimization: Investigating BBOB as a Use CaseGenetic and Evolutionary Computation Conference (GECCO'20), Jul 2020, Cancun, Mexico. ⟨10.1145/3377930.3390189⟩ |
Dynamic control parameter choices in evolutionary computationGECCO '20: Genetic and Evolutionary Computation Conference, Jul 2020, Cancún, Mexico. pp.927-956, ⟨10.1145/3377929.3389876⟩ |
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Fixed-Target Runtime AnalysisGECCO 2020 - The Genetic and Evolutionary Computation Conference, Jul 2020, Cancun, Mexico |
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Initial Design Strategies and their Effects on Sequential Model-Based Optimization An Exploratory Case Study Based on BBOBGenetic and Evolutionary Computation Conference (GECCO'20), Jul 2020, Cancun, Mexico. ⟨10.1145/3377930.3390155⟩ |
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Integrated vs. Sequential Approaches for Selecting and Tuning CMA-ES VariantsACM Genetic and Evolutionary Computation Conference (GECCO'20), ACM, Jul 2020, Cancun, Mexico. ⟨10.1145/3377930.3389831⟩ |
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Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES VariantsACM Genetic and Evolutionary Computation Conference (GECCO'20), Jul 2020, Cancun, Mexico. ⟨10.1145/3377930.3390183⟩ |
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Benchmarking and analyzing iterative optimization heuristics with IOHprofiler (GECCO'20 tutorial)Proc. of Genetic and Evolutionary Computation Conference (GECCO'20, Companion material), Jul 2020, Cancún, Mexico. pp.1043-1054, ⟨10.1145/3377929.3389879⟩ |
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Evolving Sampling Strategies for One-Shot Optimization TasksParallel Problem Solving from Nature – PPSN XVI (PPSN 2020), Sep 2020, Leiden, Netherlands. pp.111-124, ⟨10.1007/978-3-030-58112-1_8⟩ |
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Optimization of Chance-Constrained Submodular FunctionsAAAI-20 Thirty-Fourth AAAI Conference on Artificial Intelligence, Feb 2020, New York, United States |
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Benchmarking a $$(\mu +\lambda )$$ Genetic Algorithm with Configurable Crossover ProbabilityParallel Problem Solving from Nature – PPSN XVI (PPSN 2020), Sep 2020, Leiden, Netherlands. pp.699-713, ⟨10.1007/978-3-030-58115-2_49⟩ |
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Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary AlgorithmParallel Problem Solving from Nature – PPSN XVI (PPSN 2020), Sep 2020, Leiden, Netherlands. pp.485-499, ⟨10.1007/978-3-030-58115-2_34⟩ |
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Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation RatesGenetic and Evolutionary Computation Conference, Jul 2019, Prague, Czech Republic. pp.855-863, ⟨10.1145/3321707.3321827⟩ |
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Making a case for (Hyper-)parameter tuning as benchmark problemsGenetic and Evolutionary Computation Conference, Companion Material, Jul 2019, Prague, Czech Republic. pp.1755-1764, ⟨10.1145/3319619.3326857⟩ |
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Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation2019 IEEE Congress on Evolutionary Computation (CEC), Jun 2019, Wellington, New Zealand. pp.2292-2299, ⟨10.1109/CEC.2019.8790107⟩ |
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Bayesian performance analysis for black-box optimization benchmarkingGenetic and Evolutionary Computation Conference GECCO 2019, Jul 2019, Prague, Czech Republic. pp.1789-1797, ⟨10.1145/3319619.3326888⟩ |
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Illustrating the trade-off between time, quality, and success probability in heuristic searchGenetic and Evolutionary Computation Conference, Companion Material, Jul 2019, Prague, Czech Republic. pp.1807-1812, ⟨10.1145/3319619.3326895⟩ |
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Fast re-optimization via structural diversityThe Genetic and Evolutionary Computation Conference, Jul 2019, Prague, Czech Republic. pp.233-241, ⟨10.1145/3321707.3321731⟩ |
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Benchmarking discrete optimization heuristics with IOHprofilerGenetic and Evolutionary Computation Conference, Companion Material, Jul 2019, Prague, Czech Republic. pp.1798-1806, ⟨10.1145/3319619.3326810⟩ |
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Fixed-target runtime analysis of the (1 + 1) EA with resampling (student workshop paper)Genetic and Evolutionary Computation Conference, Companion Material, Jul 2019, Prague, Czech Republic. pp.2068-2071, ⟨10.1145/3319619.3326906⟩ |
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Maximizing drift is not optimal for solving OneMaxGenetic and Evolutionary Computation Conference, Companion Material, Jul 2019, Prague, Czech Republic. pp.425-426, ⟨10.1145/3319619.3321952⟩ |
Dynamic parameter choices in evolutionary computation (tutorial at GECCO 2019)Genetic and Evolutionary Computation Conference, Companion Material, Jul 2019, Prague, Czech Republic. pp.890-922, ⟨10.1145/3319619.3323372⟩ |
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Expressiveness and robustness of landscape features (student workshop paper)Genetic and Evolutionary Computation Conference, Companion Material, Jul 2019, Prague, Czech Republic. pp.2048-2051, ⟨10.1145/3319619.3326913⟩ |
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Coupling the design of benchmark with algorithm in landscape-aware solver designGenetic and Evolutionary Computation Conference, Companion Material, Jul 2019, Prague, Czech Republic. pp.1419-1420, ⟨10.1145/3319619.3326821⟩ |
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Hyper-parameter tuning for the (1 + ( λ, λ )) GAGenetic and Evolutionary Computation Conference, Jul 2019, Prague, Czech Republic. pp.889-897, ⟨10.1145/3321707.3321725⟩ |
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Online selection of CMA-ES variantsThe Genetic and Evolutionary Computation Conference, Jul 2019, Prague, Czech Republic. pp.951-959, ⟨10.1145/3321707.3321803⟩ |
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Self-adjusting mutation rates with provably optimal success rulesGECCO 2019 - The Genetic and Evolutionary Computation Conference, Jul 2019, Prague, Czech Republic. pp.1479-1487, ⟨10.1145/3321707.3321733⟩ |
Adaptive landscape analysis (student workshop paper)Genetic and Evolutionary Computation Conference, Companion Material, Jul 2019, Prague, Czech Republic. pp.2032-2035, ⟨10.1145/3319619.3326905⟩ |
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Discrepancy-based evolutionary diversity optimizationGECCO '18 - Genetic and Evolutionary Computation Conference, Jul 2018, Kyoto, France. pp.991-998, ⟨10.1145/3205455.3205532⟩ |
A Simple Proof for the Usefulness of Crossover in Black-Box OptimizationPPSN 2018: Parallel Problem Solving from Nature – PPSN XV, Sep 2018, Coimbra, Portugal. pp.29-41, ⟨10.1007/978-3-319-99259-4_3⟩ |
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Sensitivity of Parameter Control Mechanisms with Respect to Their InitializationInternational Conference on Parallel Problem Solving from Nature (PPSN 2018), Sep 2018, Coimbra, Portugal. pp.360-372, ⟨10.1007/978-3-319-99259-4_29⟩ |
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Towards an Adaptive CMA-ES ConfiguratorParallel Problem Solving from Nature – PPSN XV. PPSN 2018., Sep 2018, Coimbra, Portugal. pp.54-65, ⟨10.1007/978-3-319-99253-2_5⟩ |
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Towards a theory-guided benchmarking suite for discrete black-box optimization heuristicsGECCO '18 - Genetic and Evolutionary Computation Conference, Jul 2018, Kyoto, France. pp.951-958, ⟨10.1145/3205455.3205621⟩ |
Dynamic parameter choices in evolutionary computationGECCO '18 - Genetic and Evolutionary Computation Conference, Jul 2018, Kyoto, Japan. pp.800-830, ⟨10.1145/3205651.3207851⟩ |
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Simple on-the-fly parameter selection mechanisms for two classical discrete black-box optimization benchmark problemsGECCO '18 - Genetic and Evolutionary Computation Conference, Jul 2018, Kyoto, Japan. pp.943-950, ⟨10.1145/3205455.3205560⟩ |
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Compiling a benchmarking test-suite for combinatorial black-box optimizationGECCO '18 - Genetic and Evolutionary Computation Conference Companion, Jul 2018, Kyoto, Japan. pp.1753-1760, ⟨10.1145/3205651.3208251⟩ |
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Non-static parameter choices in evolutionary computationGECCO '17 - Genetic and Evolutionary Computation Conference, Jul 2017, Berlin, Germany. pp.736-761, ⟨10.1145/3067695.3067707⟩ |
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Unknown solution length problems with no asymptotically optimal run timeGenetic and Evolutionary Computation Conference (GECCO'17), Jul 2017, Berlin, Germany. pp.1367-1374, ⟨10.1145/3071178.3071233⟩ |
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Towards a More Practice-Aware Runtime AnalysisEA 2017 - 13th International Conference on Artificial Evolution, Oct 2017, Paris, France. pp.298-305 |
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Provably Optimal Self-Adjusting Step Sizes for Multi-Valued Decision VariablesParallel Problem Solving from Nature – PPSN XIV, Sep 2016, Edinburgh, United Kingdom. pp.782-791 |
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The Right Mutation Strength for Multi-Valued Decision VariablesGECCO 2016 - Genetic and Evolutionary Computation Conference, Jul 2016, Denver, United States. pp.1115-1122, ⟨10.1145/2908812.2908891⟩ |
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Optimal Parameter Choices via Precise Black-Box AnalysisGECCO 2016 - Genetic and Evolutionary Computation Conference, Jul 2016, Denver, United States. pp.1123-1130, ⟨10.1145/2908812.2908950⟩ |
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Tutorials at PPSN 2016PPSN 2016 - 14th International Conference on Parallel Problem Solving from Nature, Sep 2016, Edinburgh, United Kingdom. pp.1012-1022, ⟨10.1007/978-3-319-45823-6_95⟩ |
$k$-Bit Mutation with Self-Adjusting $k$ Outperforms Standard Bit MutationParallel Problem Solving from Nature – PPSN XIV, Sep 2016, Edinburgh, United Kingdom. pp.824-834 |
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The (1+1) Elitist Black-Box Complexity of LeadingOnesGECCO 2016 - Genetic and Evolutionary Computation Conference, Jul 2016, Denver, United States. pp.1131-1138, ⟨10.1145/2908812.2908922⟩ |
A Tight Runtime Analysis of the (1+(λ, λ)) Genetic Algorithm on OneMaxGECCO '15 - 2015 Annual Conference on Genetic and Evolutionary Computation, Jul 2015, Madrid, Spain. pp.1423-1430, ⟨10.1145/2739480.2754683⟩ |
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Elitist Black-Box Models: Analyzing the Impact of Elitist Selection on the Performance of Evolutionary AlgorithmsGECCO '15 - 2015 Annual Conference on Genetic and Evolutionary Computation, Jul 2015, Madrid, Spain. pp.839-846, ⟨10.1145/2739480.2754654⟩ |
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Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete SettingsGECCO '15 - 2015 Annual Conference on Genetic and Evolutionary Computation, Jul 2015, Madrid, Spain. pp.1335-1342, ⟨10.1145/2739480.2754684⟩ |
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Solving Problems with Unknown Solution Length at (Almost) No Extra CostGECCO '15 - 2015 Annual Conference on Genetic and Evolutionary Computation, Jul 2015, Madrid, Spain. pp.831-838, ⟨10.1145/2739480.2754681⟩ |
Money for Nothing: Speeding Up Evolutionary Algorithms Through Better InitializationGECCO '15 - 2015 Annual Conference on Genetic and Evolutionary Computation, Jul 2015, Madrid, Spain. pp.815-822, ⟨10.1145/2739480.2754760⟩ |
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OneMax in Black-Box Models with Several RestrictionsGECCO '15 - 2015 Annual Conference on Genetic and Evolutionary Computation, Jul 2015, Madrid, Spain. pp.1431-1438, ⟨10.1145/2739480.2754678⟩ |
The impact of random initialization on the runtime of randomized search heuristicsGECCO '14 - Conference on Genetic and Evolutionary Computation, ACM, Jul 2014, Vancouver, Canada. pp.1375-1382, ⟨10.1145/2576768.2598359⟩ |
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Unbiased black-box complexities of jump functions: how to cross large plateausGECCO '14 - Conference on Genetic and Evolutionary Computation, ACM, Jul 2014, Vancouver, Canada. pp.769-776, ⟨10.1145/2576768.2598341⟩ |
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Diffusion probabiliste dans les réseaux dynamiques15èmes Rencontres Francophones sur les Aspects Algorithmiques des Télécommunications (AlgoTel), May 2013, Pornic, France. pp.1-4 |
Lessons from the black-box: fast crossover-based genetic algorithmsProceeding of the fifteenth annual conference on Genetic and Evolutionary Computation, Jul 2013, Amsterdam, Netherlands. ⟨10.1145/2463372.2463480⟩ |
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Constructing low star discrepancy point sets with genetic algorithmsProceeding of the fifteenth annual conference on Genetic and evolutionary computation, Jul 2013, Amsterdam, Netherlands. ⟨10.1145/2463372.2463469⟩ |
Playing mastermind with many colorsProceedings of the twenty-fourth annual ACM-SIAM symposium on Discrete algorithms (SODA'13), Jan 2013, New Orleans, United States |
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Black-box complexityProceeding of the fifteenth annual conference companion, Jul 2013, Amsterdam, Netherlands. ⟨10.1145/2464576.2482680⟩ |
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Rumor Spreading in Random Evolving Graphs21st Annual European Symposium on Algorithms - ESA 2013, Sep 2013, Sophia Antipolis, France. pp.325-336, ⟨10.1007/978-3-642-40450-4_28⟩ |
Computing Minimum Cycle Bases in Weighted Partial 2-Trees in Linear TimeWG 2013 - 39th International Workshop on Graph-Theoretic Concepts in Computer Science, Jun 2013, Luebeck, Germany. pp.225-236, ⟨10.1007/978-3-642-45043-3_20⟩ |
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Playing Mastermind With Constant-Size MemorySTACS'12 (29th Symposium on Theoretical Aspects of Computer Science), Feb 2012, Paris, France. pp.441-452 |
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Fast Identification of Optimal Monotonic Classifiers2023 |
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Towards Self-Adjusting Weighted Expected Improvement for Bayesian OptimizationGECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation, Jul 2023, Lisbon, Portugal. ACM, GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pp.483-486, 2023, ⟨10.1145/3583133.3590753⟩ |
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Assessing the Generalizability of a Performance Predictive ModelGECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation, Jul 2023, Lisbon, Portugal. ACM, GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pp.311-314, 2023, ⟨10.1145/3583133.3590617⟩ |
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Comparing Algorithm Selection Approaches on Black-Box Optimization ProblemsGECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation, Jul 2023, Lisbon, Portugal. ACM, GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pp.495-498, 2023, ⟨10.1145/3583133.3590697⟩ |
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Using Irace, Paradiseo and IOHprofiler for Large-Scale Algorithm Configuration8th COSEAL workshop, Sep 2021, Online, France. 2021 |
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Benchmarking and analyzing iterative optimization heuristics with IOHprofiler (GECCO'22 tutorial slides)Autre publication scientifique hal-03718889v1 |
Optimisation Inspirée par la Nature2018 |
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Women@GECCO 2016 Chairs' WelcomeAutre publication scientifique hal-01363950v1 |
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Theory for Non-Theoreticians (Tutorial at ACM GECCO 2016)Autre publication scientifique hal-01363939v1 |
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Women@GECCO 20142014, pp.2. ⟨10.1145/2598394.2611386⟩ |
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Black-box complexity: from complexity theory to playing Mastermind2014, pp.617-640. ⟨10.1145/2598394.2605352⟩ |
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Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter ChoicesTheory of Evolutionary Computation, Springer, pp.271-321, 2020, ⟨10.1007/978-3-030-29414-4_6⟩ |
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Complexity Theory for Discrete Black-Box Optimization HeuristicsTheory of Evolutionary Computation, pp.133-212, 2019, ⟨10.1007/978-3-030-29414-4_3⟩ |
Probabilistic Lower Discrepancy Bounds for Latin Hypercube SamplesContemporary Computational Mathematics - A Celebration of the 80th Birthday of Ian Sloan, 2018, 978-3-319-72455-3 |
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Calculation of Discrepancy Measures and ApplicationsA Panorama of Discrepancy Theory, Springer, pp.621-678, 2014, 978-3-319-04695-2. ⟨10.1007/978-3-319-04696-9_10⟩ |
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Theory of Iterative Optimization Heuristics: From Black-Box Complexity over Algorithm Design to Parameter ControlNeural and Evolutionary Computing [cs.NE]. Sorbonne Université, 2020 |
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Parallel Problem Solving from Nature - PPSN XVI - 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part IPPSN XVI - 16th International Conference on Parallel Problem Solving from Nature, Sep 2020, Leiden, Netherlands. 12269, Springer, 2020, Lecture Notes in Computer Science, ⟨10.1007/978-3-030-58112-1⟩ |
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Parallel Problem Solving from Nature - PPSN XVI - 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part IIPPSN XVI - 16th International Conference on Parallel Problem Solving from Nature, Sep 2020, Leiden, Netherlands. 12270, Springer, 2020, Lecture Notes in Computer Science, ⟨10.1007/978-3-030-58115-2⟩ |