Publications 2013-2026

• 2026
  • D. Antipov, C. Doerr : “When Switching Algorithms Helps: A Theoretical Study of Online Algorithm Selection”, GECCO '26: Proceedings of the Genetic and Evolutionary Computation Conference, San Jose, Costa Rica, pp. 1304-1312, (Association for Computing Machinery), (ISBN: 9798400724879) (2026)
  • C.‑V. Dinu, D. Vermetten, C. Doerr : “Similarity-based Portfolio Construction for Black-box Optimization”, GECCO '26: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '26, San José, Costa Rica, pp. 880-888, (Association for Computing Machinery), (ISBN: 9798400724879) (2026)
  • Ch. Artigues, N. Brauner, P. Fouilhoux, N. Absi, A. Nur Arslan, O. Bellenguez, F. Bendali, Th. Benoist, C. Bentz, T. Berthold, M. Besançon, J.‑Ch. Billaut, P. Bonami, E. Bourreau, S. Buchet, S. Cafieri, F. Clautiaux, C. D’ambrosio, S. Dauzère‑Pérès, S. De Givry, C. Doerr, B. Escoffier, D. Feillet, R. Figueiredo, X. Gandibleux, B. Gaujal, J.‑Ph. Gayon, C. Gicquel, A. Goëffon, R. Grappe, C. Grelier, E. Hébrard, E. Hyon, G. Katsirelos, S. Kedad‑Sidhoum, Ph. Lacomme, J.‑M. Lagniez, A. Lambert, S. Lannez, V. Leclere, Ch. Lecoutre, B. Legat, S. Leyffer, A. Liefooghe, I. Ljubic, G. Lucarelli, F. Messine, F. Meunier, S. Moretti, V. Mousseau, A. Munier‑Kordon, J.‑M. Nicod, F. Pacaud, A. Parmentier, F. Pascual, P. Perny, L. Perron, Ch. Picouleau, D. Olivier Fernandez Pons, M. Poss, G. Poveda, Ch. Prud’homme, F. Roupin, Th. Schiex, M. Sevaux, D. Trystram, A. Tsoukiàs, Ch. Vanaret : “Rapport de Prospective du GDR Recherche Opérationnelle et Décision (ROD)”, 76 p. pages (2026)
  • M. Santoni, E. Raponi, A. Neumann, F. Neumann, M. Preuss, C. Doerr : “Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization”, Evolutionary Computation, vol. 34 (2), pp. 213–233, (Massachusetts Institute of Technology Press (MIT Press)) (2026)
  • O. Fourquet, D. Afenteva, K. Zhang, S. Hautaniemi, M. Krejca, C. Doerr, B. Schwikowski : “Identification of Monotonically Classifying Pairs of Genes for Ordinal Disease Outcomes”, Bioinformatics Advances, vol. 6 (1), pp. vbag143, (Oxford academic) (2026)
  • S. Mladenovic, M. Lindauer, C. Doerr : “Automated Data Preparation for Machine Learning: A Survey”, Journal of Data-centric Machine Learning Research, (MLR Press) (2026)
  • G. Cenikj, A. Nikolikj, G. Petelin, N. Van Stein, C. Doerr, T. Eftimov : “A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization”, Swarm and Evolutionary Computation, vol. 101, pp. 102288, (Elsevier) (2026)
  • L. Akoglu, C. Doerr, J. Van Rijn, R. Garnett, J. Gardner : “Proceedings of the Fourth International Conference on Automated Machine Learning”, Proceedings of Machine Learning Research, vol. 293, Proceedings of Machine Learning Research, New York, NY, United States, (PMLR) (2026)
  • C. Benjamins, H. Graf, S. Segel, D. Deng, T. Ruhkopf, L. Hennig, S. Basu, N. Mallik, E. Bergman, D. Chen, F. Clément, A. Tornede, M. Feurer, K. Eggensperger, F. Hutter, C. Doerr, M. Lindauer : “carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks”, Transactions on Machine Learning Research, ([Amherst Massachusetts]: OpenReview.net, 2022) (2026)
  • T. Nguyen, Ph. Le, A. Biedenkapp, C. Doerr, N. Dang : “Deep Reinforcement Learning for Dynamic Algorithm Configuration: A Case Study on Optimizing OneMax with the $(1+(\lambda,\lambda))$-GA”, ACM Transactions on Evolutionary Learning and Optimization, (ACM) (2026)
  • Y. Lu, C. Doerr, M. Sebilo : “Geochemical Fingerprinting and Machine Learning for Authenticating Sparkling Wine Origins”, npj Science of Food, vol. 10 (1), pp. 109, (Springer Nature) (2026)
• 2025
  • Y. Lu, C. Doerr, M. Sebilo : “From Binary to Multiclass Logistic Regression for Wine Origin : A Correlation-and Cost-Aware Study in Portugal and Chile”, 2025 IEEE International Conference on Big Data (BigData), Macao, Macao, pp. 6800-6807, (IEEE), (ISBN: 979-8-3315-9447-3) (2025)
  • A. Kostovska, D. Vermetten, P. Korosec, S. Dzeroski, C. Doerr, T. Eftimov : “Using Machine Learning Methods to Assess Module Performance Contribution in Modular Optimization Frameworks”, Evolutionary Computation, vol. 33 (4), pp. 485–512, (Massachusetts Institute of Technology Press (MIT Press)) (2025)
  • M. López‑Ibañez, D. Vermetten, J. Dréo, C. Doerr : “Using the Empirical Attainment Function for Analyzing Single-objective Black-box Optimization Algorithms”, IEEE Transactions on Evolutionary Computation, vol. 29 (5), pp. 1774-1782, (Institute of Electrical and Electronics Engineers) (2025)
  • O. Fourquet, M. Krejca, C. Doerr, B. Schwikowski : “Towards the genome-scale discovery of bivariate monotonic classifiers”, BMC Bioinformatics, vol. 26 (1), pp. 228, (BioMed Central) (2025)
  • G. Covini, D. Antipov, C. Doerr : “Enhancing Parameter Control Policies with State Information”, Proc. of 18^th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (FOGA), Leiden, Netherlands (2025)
  • M. López‑Ibañez, D. Vermetten, J. Dréo, C. Doerr : “Analyzing Single-objective Black-box Optimization Algorithms Using the Empirical Attainment Function”, Genetic and Evolutionary Computation Conference, Malaga, Spain (2025)
  • T. Nguyen, Ph. Le, A. Biedenkapp, C. Doerr, N. Dang : “On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+(L,L))-GA”, GECCO '25: Proceedings of the Genetic and Evolutionary Computation Conference, Malaga, Spain, pp. 1162-1171, (Association for Computing Machinery), (ISBN: 979-8-4007-1465-8) (2025)
  • F. Clément, C. Doerr, K. Klamroth, L. Paquete : “Constructing Optimal Star Discrepancy Sets”, Proceedings of the American Mathematical Society, Series B, vol. 12 (7), pp. 78-90 (2025)
  • F. Clément, C. Doerr, K. Klamroth, L. Paquete : “Searching permutations for constructing uniformly distributed point sets”, Proceedings of the National Academy of Sciences of the United States of America, vol. 122 (14), pp. e2424464122, (National Academy of Sciences) (2025)
  • D. Vermetten, F. Ye, Th. Bäck, C. Doerr : “MA-BBOB: A Problem Generator for Black-Box Optimization Using Affine Combinations and Shifts”, ACM Transactions on Evolutionary Learning and Optimization, vol. 5 (1), pp. 1-19, (ACM) (2025)
  • G. Cenikj, G. Petelin, C. Doerr, P. Korosec, T. Eftimov : “Beyond Landscape Analysis: DynamoRep Features For Capturing Algorithm-Problem Interaction In Single-Objective Continuous Optimization”, Evolutionary Computation, pp. 1-28, (Massachusetts Institute of Technology Press (MIT Press)) (2025)
  • D. Vermetten, J. Rook, O. Preuß, J. De Nobel, C. Doerr, M. López‑Ibañez, H. Trautmann, Th. Bäck : “MO-IOHinspector: Anytime Benchmarking of Multi-Objective Algorithms using IOHprofiler”, Evolutionary Multi-Criterion Optimization, vol. 15512, Lecture Notes in Computer Science, Canberra, Australia, pp. 242-256, (Springer), (ISBN: 978-981-96-3506-1) (2025)
  • M. Seiler, U. Skvorc, C. Doerr, H. Trautmann : “Synergies of Deep and Classical Exploratory Landscape Features for Automated Algorithm Selection”, Lecture Notes in Computer Science, vol. 14990, Lecture Notes in Computer Science, Ischia, Italy, pp. 361-376, (Springer), (ISBN: 978-3-031-75623-8) (2025)
  • E. Raponi, N. Rakotonirina, J. Rapin, C. Doerr, O. Teytaud : “Optimizing With Low Budgets: A Comparison On the Black-Box Optimization Benchmarking Suite and OpenAI Gym”, IEEE Transactions on Evolutionary Computation, vol. 29 (1), pp. 91-101, (Institute of Electrical and Electronics Engineers) (2025)
  • M. Santoni, Ch. Dürr, C. Doerr, M. Preuss, E. Raponi : “Cascading CMA-ES Instances for Generating Input-diverse Solution Batches”, Proc. of the Genetic and Evolutionary Computation Conference (GECCO, Companion material, BBOB workshop), Malaga, Spain (2025)
  • A. Kostovska, C. Doerr, S. Dzeroski, P. Panov, T. Eftimov : “Geometric Learning in Black-Box Optimization: A GNN Framework for Algorithm Performance Prediction”, Proc. of the Genetic and Evolutionary Computation Conference (GECCO, Companion material), Malaga, Spain (2025)
  • T. Nguyen, Ph. Le, C. Doerr, N. Dang : “Multi-parameter Control for the (1+(L,L))-GA on OneMax via Deep Reinforcement Learning”, Proc. of Foundations of Genetic Algorithms (FOGA), Leiden, Netherlands (2025)
• 2024
  • M. Van den Nieuwenhuijzen, C. Doerr, J. Van Rijn, H. Gouk : “Selecting Pre-trained Models for Transfer Learning with Data-centric Meta-features”, AutoML Conference 2024 (Workshop Track), Paris, France (2024)
  • D. Vermetten, J. Lengler, D. Rusin, Th. Bäck, C. Doerr : “Empirical Analysis of the Dynamic Binary Value Problem with IOHprofiler”, Parallel Problem Solving from Nature – PPSN XVIII, vol. 15149, Lecture Notes in Computer Science, Hagenberg, Austria, pp. 20-35, (Springer Nature Switzerland), (ISBN: 978-3-031-70068-2) (2024)
  • K. Dietrich, R. Prager, C. Doerr, H. Trautmann : “Hybridizing Target-and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box Optimization”, Parallel Problem Solving from Nature – PPSN XVIII (PPSN 2024), vol. 15149, Lecture Notes in Computer Science, Hagenberg, Austria, pp. 154-169, (Springer Nature Switzerland) (2024)
  • M. Seiler, U. Skvorc, G. Cenikj, C. Doerr, H. Trautmann : “Learned Features vs. Classical ELA on Affine BBOB Functions”, Parallel Problem Solving from Nature – PPSN XVIII (PPSN 2024), vol. 15149, Lecture Notes in Computer Science, Hagenberg, Austria, pp. 137-153, (Springer Nature Switzerland) (2024)
  • A. Nikolikj, A. Kostovska, G. Cenikj, C. Doerr, T. Eftimov : “Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks”, 2024 IEEE Congress on Evolutionary Computation (CEC), Yokohama, Japan, pp. 1-8, (IEEE), (ISBN: 979-8-3503-0836-5) (2024)
  • F. Clément, C. Doerr, L. Paquete : “Heuristic approaches to obtain low-discrepancy point sets via subset selection”, Journal of Complexity, vol. 83, pp. 101852, (Elsevier) (2024)
  • C. Doerr, D. Janett, J. Lengler : “Tight Runtime Bounds for Static Unary Unbiased Evolutionary Algorithms on Linear Functions”, Algorithmica, vol. 86 (10), pp. 3115-3152, (Springer Verlag) (2024)
  • K. Dietrich, D. Vermetten, C. Doerr, P. Kerschke : “Impact of Training Instance Selection on Automated Algorithm Selection Models for Numerical Black-box Optimization”, GECCO '24: Proceedings of the Genetic and Evolutionary Computation Conference, Melbourne, Australia, pp. 1007-1016, (ISBN: 9798400704949) (2024)
  • D. Vermetten, C. Doerr, H. Wang, A. Kononova, Th. Bäck : “Large-scale Benchmarking of Metaphor-based Optimization Heuristics”, GECCO '24: Proceedings of the Genetic and Evolutionary Computation Conference, Melbourne, Australia, pp. 41-49, (ACM), (ISBN: 9798400704949) (2024)
  • A. Nikolikj, A. Kostovska, D. Vermetten, C. Doerr, T. Eftimov : “Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks”, 2024 IEEE Congress on Evolutionary Computation (CEC), Yokohama, Japan, pp. 1-8, (IEEE), (ISBN: 979-8-3503-0836-5) (2024)
  • J. De Nobel, F. Ye, D. Vermetten, H. Wang, C. Doerr, Th. Bäck : “IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics”, Evolutionary Computation, pp. 1-6, (Massachusetts Institute of Technology Press (MIT Press)) (2024)
  • M. Santoni, E. Raponi, R. De Leone, C. Doerr : “Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB”, ACM Transactions on Evolutionary Learning and Optimization, vol. 4 (3), (ACM) (2024)
• 2023
  • A. Kostovska, D. Vermetten, C. Doerr, S. Dzeroski, T. Eftimov : “OPTION: OPTImization Algorithm Benchmarking ONtology”, IEEE Transactions on Evolutionary Computation, vol. 27 (6), pp. 1618-1632, (Institute of Electrical and Electronics Engineers) (2023)
  • C. Doerr, M. Krejca : “Run Time Analysis for Random Local Search on Generalized Majority Functions”, IEEE Transactions on Evolutionary Computation, vol. 27 (5), pp. 1385-1397, (Institute of Electrical and Electronics Engineers) (2023)
  • A. Nikolikj, M. Pluháček, C. Doerr, P. Korosec, T. Eftimov : “Sensitivity Analysis of RF+clust for Leave-One-Problem-Out Performance Prediction”, 2023 IEEE Congress on Evolutionary Computation (CEC), Chicago, IL, United States, pp. 1-8, (IEEE), (ISBN: 979-8-3503-1458-8) (2023)
  • D. Chen, M. Buzdalov, C. Doerr, N. Dang : “Using Automated Algorithm Configuration for Parameter Control”, FOGA '23: Proceedings of the 17^th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, Potsdam, Germany, pp. 38-49, (ACM), (ISBN: 979-8-4007-0202-0) (2023)
  • A. Nikolikj, G. Cenikj, G. Ispirova, D. Vermetten, R. Lang, A. Engelbrecht, C. Doerr, P. Korosec, T. Eftimov : “Assessing the Generalizability of a Performance Predictive Model”, GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal, pp. 311-314, (ACM) (2023)
  • A. Kostovska, A. Janković, D. Vermetten, S. Dzeroski, T. Eftimov, C. Doerr : “Comparing Algorithm Selection Approaches on Black-Box Optimization Problems”, GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal, pp. 495-498, (ACM) (2023)
  • M. Santoni, E. Raponi, R. De Leone, C. Doerr : “Comparison of Bayesian Optimization Algorithms for BBOB Problems in Dimensions 10 and 60”, GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal, pp. 2390-2393, (ACM), (ISBN: 979-8-4007-0120-7) (2023)
  • C. Benjamins, E. Raponi, A. Janković, C. Doerr, M. Lindauer : “Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization”, GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal, pp. 483-486, (ACM) (2023)
  • C. Doerr, H. Wang, D. Vermetten, Th. Bäck, J. De Nobel, F. Ye : “Benchmarking and analyzing iterative optimization heuristics with IOHprofiler”, GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal, pp. 938-945, (ACM) (2023)
  • A. Nikolikj, S. Dzeroski, M. Muñoz, C. Doerr, P. Korosec, T. Eftimov : “Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances”, GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference, Lisbon, Portugal, pp. 529-537, (ACM), (ISBN: 9798400701191) (2023)
  • G. Cenikj, G. Petelin, C. Doerr, P. Korosec, T. Eftimov : “DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems”, GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference, Lisbon, Portugal, pp. 813-821, (ACM), (ISBN: 9798400701191) (2023)
  • C. Doerr, D. Janett, J. Lengler : “Tight Runtime Bounds for Static Unary Unbiased Evolutionary Algorithms on Linear Functions”, Proceedings of the Genetic and Evolutionary Computation Conference, Lisbon, Portugal, pp. 1565-1574, (ACM) (2023)
  • D. Vermetten, F. Ye, C. Doerr : “Using Affine Combinations of BBOB Problems for Performance Assessment”, Proceedings of the Genetic and Evolutionary Computation Conference, Lisbon, Portugal, pp. 873-881, (ACM), (ISBN: 9798400701191) (2023)
  • A. Nikolikj, C. Doerr, T. Eftimov : “RF+clust for Leave-One-Problem-Out Performance Prediction”, Applications of Evolutionary Computation, vol. 13989, Lecture Notes in Computer Science, Brno, Czechia, pp. 285-301, (Springer), (ISBN: 978-3-031-30229-9) (2023)
  • A. Kostovska, D. Vermetten, S. Dzeroski, P. Panov, T. Eftimov, C. Doerr : “Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms”, Applications of Evolutionary Computation (EvoApplications 2023), vol. 13989, Lecture Notes in Computer Science, Brno, Czechia, pp. 253-268, (Springer), (ISBN: 978-3-031-30229-9) (2023)
  • O. Fourquet, M. Krejca, C. Doerr, B. Schwikowski : “Fast Identification of Optimal Monotonic Classifiers”, (2023)
  • A. Kostovska, C. Doerr, S. Dzeroski, D. Kocev, P. Panov, T. Eftimov : “Explainable Model-specific Algorithm Selection for Multi-Label Classification”, Proc. of 2022 IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, Singapore, pp. 39-46, (IEEE), (ISBN: 978-1-6654-8768-9) (2023)
  • D. Vermetten, F. Ye, Th. Bäck, C. Doerr : “MA-BBOB: Many-Affine Combinations of BBOB Functions for Evaluating AutoML Approaches in Noiseless Numerical Black-Box Optimization Contexts”, Proceedings of Machine Learning Research (PMLR), Potsdam, Germany (2023)
  • A. Kostovska, G. Cenikj, D. Vermetten, A. Janković, A. Nikolikj, U. Skvorc, P. Korosec, C. Doerr, T. Eftimov : “PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization”, Proceedings of Machine Learning Research (PMLR), Potsdam, Germany (2023)
  • C. Benjamins, E. Raponi, A. Janković, C. Doerr, M. Lindauer : “Self-Adjusting Weighted Expected Improvement for Bayesian Optimization”, Proceedings of Machine Learning Research (PMLR), Potsdam, Germany (2023)
  • F. Clément, D. Vermetten, J. De Nobel, A. Jesus, L. Paquete, C. Doerr : “Computing Star Discrepancies with Numerical Black-Box Optimization Algorithms”, GECCO '23: Genetic and Evolutionary Computation Conference, Lisbon, Portugal, pp. 1330-1338, (ACM) (2023)
• 2022
  • Th. Bäck, C. Doerr, B. Sendhoff, Th. Stützle : “Guest Editorial Special Issue on Benchmarking Sampling-Based Optimization Heuristics: Methodology and Software”, IEEE Transactions on Evolutionary Computation, vol. 26 (6), pp. 1202-1205, (Institute of Electrical and Electronics Engineers) (2022)
  • C. Benjamins, E. Raponi, A. Janković, K. Van der Blom, M. Santoni, M. Lindauer, C. Doerr : “PI is back! Switching Acquisition Functions in Bayesian Optimization”, 2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, New Orleans, United States (2022)
  • C. Benjamins, A. Janković, E. Raponi, K. Van der Blom, M. Lindauer, C. Doerr : “Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis”, 6^th Workshop on Meta-Learning at NeurIPS 2022, New Orleans, United States (2022)
  • F. Clément, C. Doerr, L. Paquete : “Star Discrepancy Subset Selection: Problem Formulation and Efficient Approaches for Low Dimensions”, Journal of Complexity, vol. 70, pp. 101645, (Elsevier) (2022)
  • H. Wang, D. Vermetten, F. Ye, C. Doerr, Th. Bäck : “IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics”, ACM Transactions on Evolutionary Learning and Optimization, vol. 2 (1), pp. 3:1-3:29, (ACM) (2022)
  • F. Ye, C. Doerr, H. Wang, Th. Bäck : “Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance”, IEEE Transactions on Evolutionary Computation, vol. 26 (6), pp. 1526-1538, (Institute of Electrical and Electronics Engineers) (2022)
  • L. Meunier, H. Rakotoarison, P. Wong, B. Roziere, J. Rapin, O. Teytaud, A. Moreau, C. Doerr : “Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking”, IEEE Transactions on Evolutionary Computation, vol. 26 (3), pp. 490-500, (Institute of Electrical and Electronics Engineers) (2022)
  • K. Antonov, E. Raponi, H. Wang, C. Doerr : “High Dimensional Bayesian Optimization with Kernel Principal Component Analysis”, 17^th Proceedings of Parallel Problem Solving from Nature - (PPSN) 2022, Dortmund, Germany, pp. 118-131 (2022)
  • R. Trajanov, A. Nikolikj, G. Cenikj, F. Teytaud, M. Videau, O. Teytaud, T. Eftimov, M. López‑Ibañez, C. Doerr : “Improving Nevergrad’s Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration”, Proc. of Parallel Problem Solving from Nature - PPSN XVII, Dortmund, Germany, pp. 18-31 (2022)
  • F. Ye, D. Vermetten, C. Doerr, Th. Bäck : “Non-Elitist Selection Can Improve the Performance of Irace”, 17^th Proceedings of Parallel Problem Solving from Nature - (PPSN) 2022, Dortmund, Germany, pp. 32-45 (2022)
  • N. Bulanova, A. Buzdalova, C. Doerr : “Fast Re-Optimization of LeadingOnes with Frequent Changes”, 2022 IEEE Congress on Evolutionary Computation (CEC), 2022 IEEE Congress on Evolutionary Computation (CEC), Padua, Italy, pp. 1-8, (IEEE) (2022)
  • A. Janković, D. Vermetten, A. Kostovska, J. De Nobel, T. Eftimov, C. Doerr : “Trajectory-based Algorithm Selection with Warm-starting”, 2022 IEEE Congress on Evolutionary Computation (CEC), 2022 IEEE Congress on Evolutionary Computation (CEC), Padua, Italy, pp. 1-8, (IEEE) (2022)
  • D. Vermetten, H. Wang, M. López‑Ibañez, C. Doerr, Th. Bäck : “Analyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary Algorithms”, GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference, Boston, United States (2022)
  • Q. Renau, J. Dréo, A. Peres, Y. Semet, C. Doerr, B. Doerr : “Automated Algorithm Selection for Radar Network Configuration”, GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference, Boston, United States (2022)
  • G. Cenikj, R. Lang, A. Engelbrecht, C. Doerr, P. Korosec, T. Eftimov : “SELECTOR: Selecting a Representative Benchmark Suite for Reproducible Statistical Comparison”, GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference, Boston, United States (2022)
  • A. Kostovska, D. Vermetten, S. Dzeroski, C. Doerr, P. Korosec, T. Eftimov : “The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants”, GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference, Boston, United States (2022)
  • A. Biedenkapp, N. Dang, M. Krejca, F. Hutter, C. Doerr : “Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration”, GECCO '22: Genetic and Evolutionary Computation Conference, Boston, United States, pp. 766-775 (2022)
  • A. Kostovska, A. Janković, D. Vermetten, J. De Nobel, H. Wang, T. Eftimov, C. Doerr : “Per-run Algorithm Selection with Warm-starting using Trajectory-based Features”, 17^th Proceedings of Parallel Problem Solving from Nature - (PPSN) 2022, Dortmund, Germany, pp. 46-60 (2022)
  • C. Doerr, H. Wang, D. Vermetten, Th. Bäck, J. De Nobel, F. Ye : “Benchmarking and analyzing iterative optimization heuristics with IOHprofiler (GECCO’22 tutorial slides)”, (2022)
  • M. Buzdalov, B. Doerr, C. Doerr, D. Vinokurov : “Fixed-Target Runtime Analysis”, Algorithmica, vol. 84 (6), pp. 1762-1793, (Springer Verlag) (2022)
• 2021
  • B. Doerr, C. Doerr, J. Lengler : “Self-Adjusting Mutation Rates with Provably Optimal Success Rules”, Algorithmica, vol. 83 (10), pp. 3108-3147, (Springer Verlag) (2021)
  • J. Dréo, C. Doerr, A. Aziz‑Alaoui, A. Zheng : “Using Irace, Paradiseo and IOHprofiler for Large-Scale Algorithm Configuration”, 8^th COSEAL workshop, Online, France (2021)
  • F. Ye, C. Doerr, Th. Bäck : “Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection”, Proc. of Genetic and Evolutionary Computation Conference (GECCO 2021, Companion Material), Lille, France (2021)
  • A. Kostovska, D. Vermetten, C. Doerr, S. Dzeroski, P. Panov, T. Eftimov : “OPTION: OPTImization Algorithm Benchmarking ONtology”, Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2021, Companion Material), Lille (on line), France (2021)
  • T. Eftimov, A. Janković, G. Popovski, C. Doerr, P. Korosec : “Personalizing Performance Regression Models to Black-Box Optimization Problems”, Genetic and Evolutionary Computation Conference (GECCO 2021), Proc. of Genetic and Evolutionary Computation Conference (GECCO 2021), Lille, France (2021)
  • A. Janković, G. Popovski, T. Eftimov, C. Doerr : “The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection”, Genetic and Evolutionary Computation Conference (GECCO 2021), Lille, France, pp. 687-696, (Association for Computing Machinery) (2021)
  • J. De Nobel, D. Vermetten, H. Wang, C. Doerr, Th. Bäck : “Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules”, Genetic and Evolutionary Computation Conference (GECCO 2021, Companion Material, Workshop), Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2021, Companion Material, Workshop Paper), Lille (en ligne), France (2021)
  • K. Antonov, M. Buzdalov, A. Buzdalova, C. Doerr : “Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms”, IEEE Congress on Evolutionary Computation (CEC'21), Krakow, Poland (2021)
  • Q. Renau, J. Dréo, C. Doerr, B. Doerr : “Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions”, Applications of Evolutionary Computation 24^th International Conference, EvoApplications 2021, Held as Part of EvoStar 2021, Virtual Event, April 7–9, 2021, Proceedings, vol. 12694, Lecture Notes in Computer Science, Sevilla (Virtual), Spain, pp. 17-33 (2021)
  • A. Janković, T. Eftimov, C. Doerr : “Towards Feature-Based Performance Regression Using Trajectory Data”, Applications of Evolutionary Computation 24^th International Conference, EvoApplications 2021, Held as Part of EvoStar 2021, Virtual Event, April 7–9, 2021, Proceedings, vol. 12694, Lecture Notes in Computer Science, Sevilla (on line), Spain, pp. 601-617, (Springer) (2021)
  • M. El Yafrani, M. Scoczynski, I. Sung, M. Wagner, C. Doerr, P. Nielsen : “MATE: A Model-based Algorithm Tuning Engine - A proof of concept towards transparent feature-dependent parameter tuning using symbolic regression”, Evolutionary Computation in Combinatorial Optimization 21^st European Conference, EvoCOP 2021, Held as Part of EvoStar 2021, Virtual Event, April 7–9, 2021, Proceedings, vol. 12692, Lecture Notes in Computer Science, Sevilla (on line), Spain, pp. 51-67, (Springer) (2021)
  • N. Buskulic, C. Doerr : “Maximizing Drift is Not Optimal for Solving OneMax”, Evolutionary Computation, pp. 1-20, (Massachusetts Institute of Technology Press (MIT Press)) (2021)
  • M. Buzdalov, C. Doerr : “Optimal Static Mutation Strength Distributions for the (1 + λ) Evolutionary Algorithm on OneMax”, Proc. of Genetic and Evolutionary Computation Conference (GECCO 2021), Lille, France, pp. 660-668, (Association for Computing Machinery) (2021)
  • A. Aziz‑Alaoui, C. Doerr, J. Dréo : “Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks”, Proc. of Genetic and Evolutionary Computation Conference (GECCO 2021, Companion Material, Workshop paper), Lille, France (2021)
• 2020
  • K. Antonov, A. Buzdalova, C. Doerr : “Mutation Rate Control in the (1 + λ) Evolutionary Algorithm with a Self-adjusting Lower Bound”, Mathematical Optimization Theory and Operations Research 19^th International Conference, MOTOR 2020, Novosibirsk, Russia, July 6–10, 2020, Revised Selected Papers, vol. 1275, Communications in Computer and Information Science, Novosibirsk, Russian Federation, pp. 305-319 (2020)
  • F. Ye, H. Wang, C. Doerr, Th. Bäck : “Benchmarking a $$(\mu +\lambda )$$ Genetic Algorithm with Configurable Crossover Probability”, Parallel Problem Solving from Nature – PPSN XVI (PPSN 2020), vol. 12270, Lecture Notes in Computer Science, Leiden, Netherlands, pp. 699-713, (Springer) (2020)
  • Q. Renau, C. Doerr, J. Dréo, B. Doerr : “Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy”, Parallel Problem Solving from Nature – PPSN XVI, vol. 12270, Lecture Notes in Computer Science, Leiden, Netherlands, pp. 139-153, (Springer) (2020)
  • A. Buzdalova, C. Doerr, A. Rodionova : “Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm”, Parallel Problem Solving from Nature – PPSN XVI, vol. 12270, Lecture Notes in Computer Science, Leiden, Netherlands, pp. 485-499, (Springer) (2020)
  • M. Buzdalov, C. Doerr : “Optimal Mutation Rates for the $(1+\lambda )$ EA on OneMax”, Parallel Problem Solving from Nature – PPSN XVI, vol. 12270, Lecture Notes in Computer Science, Leiden, Netherlands, pp. 574-587, (Springer) (2020)
  • J. Bossek, C. Doerr, P. Kerschke, A. Neumann, F. Neumann : “Evolving Sampling Strategies for One-Shot Optimization Tasks”, Parallel Problem Solving from Nature – PPSN XVI, vol. 12269, Lecture Notes in Computer Science, Leiden, Netherlands, pp. 111-124, (Springer) (2020)
  • E. Raponi, H. Wang, M. Bujny, S. Boria, C. Doerr : “High Dimensional Bayesian Optimization Assisted by Principal Component Analysis”, Parallel Problem Solving from Nature – PPSN XVI, vol. 12269, Lecture Notes in Computer Science, Leiden, Netherlands, pp. 169-183, (Springer) (2020)
  • L. Meunier, C. Doerr, J. Rapin, O. Teytaud : “Variance Reduction for Better Sampling in Continuous Domains”, Parallel Problem Solving from Nature – PPSN XVI, vol. 12269, Lecture Notes in Computer Science, Leiden, Netherlands, pp. 154-168, (Springer) (2020)
  • J. Bossek, C. Doerr, P. Kerschke : “Initial Design Strategies and their Effects on Sequential Model-Based Optimization An Exploratory Case Study Based on BBOB”, Genetic and Evolutionary Computation Conference (GECCO'20), Cancun, Mexico (2020)
  • D. Vermetten, H. Wang, Th. Bäck, C. Doerr : “Towards Dynamic Algorithm Selection for Numerical Black-Box Optimization: Investigating BBOB as a Use Case”, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'20), Cancun, Mexico (2020)
  • H. Wang, C. Doerr, O. Shir, Th. Bäck : “Benchmarking and analyzing iterative optimization heuristics with IOHprofiler (GECCO’20 tutorial)”, Proc. of Genetic and Evolutionary Computation Conference (GECCO'20, Companion material), Cancún, Mexico, pp. 1043-1054, (ACM) (2020)
  • G. Papa, C. Doerr : “Dynamic control parameter choices in evolutionary computation”, GECCO '20: Genetic and Evolutionary Computation Conference, Proc. of Genetic and Evolutionary Computation Conference (GECCO'20, Companion Material), Cancún, Mexico, pp. 927-956, (ACM) (2020)
  • C. Doerr, F. Ye, N. Horesh, H. Wang, O. Shir, Th. Bäck : “Benchmarking discrete optimization heuristics with IOHprofiler”, Applied Soft Computing, vol. 88, pp. 106027, (Elsevier) (2020)
  • C. Doerr, C. Fonseca, T. Friedrich, X. Yao : “Theory of Randomized Optimization Heuristics (Report of Dagstuhl Seminar 19431)”, Dagstuhl Reports, vol. 9 (10), pp. 61-94, (Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik) (2020)
  • B. Doerr, C. Doerr, J. Yang : “Optimal parameter choices via precise black-box analysis”, Theoretical Computer Science, vol. 801, pp. 1-34, (Elsevier) (2020)
  • T. Eftimov, G. Popovski, Q. Renau, P. Korosec, C. Doerr : “Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes”, 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, Australia, pp. 775-782, (IEEE) (2020)
  • Th. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, H. Trautmann : “Parallel Problem Solving from Nature - PPSN XVI - 16^th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I”, PPSN XVI - 16^th International Conference on Parallel Problem Solving from Nature, vol. 12269, Lecture Notes in Computer Science, Leiden, Netherlands, (Springer) (2020)
  • Th. Bäck, M. Preuss, A. Deutz, H. Wang, C. Doerr, M. Emmerich, H. Trautmann : “Parallel Problem Solving from Nature - PPSN XVI - 16^th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part II”, PPSN XVI - 16^th International Conference on Parallel Problem Solving from Nature, vol. 12270, Lecture Notes in Computer Science, Leiden, Netherlands, (Springer) (2020)
  • M. Buzdalov, B. Doerr, C. Doerr, D. Vinokurov : “Fixed-Target Runtime Analysis”, GECCO 2020 - The Genetic and Evolutionary Computation Conference, Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO'20), Cancun, Mexico (2020)
  • D. Vermetten, H. Wang, C. Doerr, Th. Bäck : “Integrated vs. Sequential Approaches for Selecting and Tuning CMA-ES Variants”, ACM Genetic and Evolutionary Computation Conference (GECCO'20), Proc. of ACM Genetic and Evolutionary Computation Conference (GECCO'20), Cancun, Mexico (2020)
  • A. Janković, C. Doerr : “Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants”, GECCO'20 Proceedings of the ACM Genetic and Evolutionary Computation Conference, GECCO 2020 Proceedings, Cancun, Mexico, (ACM) (2020)
  • B. Doerr, C. Doerr, A. Neumann, F. Neumann, A. Sutton : “Optimization of Chance-Constrained Submodular Functions”, AAAI-20 Thirty-Fourth AAAI Conference on Artificial Intelligence, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI'20), New York, United States (2020)
  • B. Doerr, C. Doerr : “Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices”, chapter in Theory of Evolutionary Computation, pp. 271-321, (Springer) (2020)
• 2019
  • C. Doerr : “Complexity Theory for Discrete Black-Box Optimization Heuristics”, chapter in Theory of Evolutionary Computation, pp. 133-212 (2019)
  • A. Janković, C. Doerr : “Adaptive landscape analysis (student workshop paper)”, Genetic and Evolutionary Computation Conference, Companion Material, Prague, Czechia, pp. 2032-2035, (ACM Press) (2019)
  • C. Doerr, F. Ye, N. Horesh, H. Wang, O. Shir, Th. Bäck : “Benchmarking discrete optimization heuristics with IOHprofiler”, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Prague, Czechia, pp. 1798-1806, (ACM Press) (2019)
  • J. Dréo, C. Doerr, Y. Semet : “Coupling the design of benchmark with algorithm in landscape-aware solver design”, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Prague, Czechia, pp. 1419-1420, (ACM Press) (2019)
  • C. Doerr : “Dynamic parameter choices in evolutionary computation (tutorial at GECCO 2019)”, Genetic and Evolutionary Computation Conference, Companion Material, Prague, Czechia, pp. 890-922, (ACM Press) (2019)
  • Q. Renau, J. Dréo, C. Doerr, B. Doerr : “Expressiveness and robustness of landscape features (student workshop paper)”, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Prague, Czechia, pp. 2048-2051, (ACM Press) (2019)
  • D. Vinokurov, M. Buzdalov, A. Buzdalov, B. Doerr, C. Doerr : “Fixed-target runtime analysis of the (1 + 1) EA with resampling (student workshop paper)”, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Prague, Czechia, pp. 2068-2071, (ACM Press) (2019)
  • N. Dang, C. Doerr : “Hyper-parameter tuning for the (1 + ( λ, λ )) GA”, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference, Prague, Czechia, pp. 889-897, (ACM Press) (2019)
  • I. Ignashov, A. Buzdalova, M. Buzdalov, C. Doerr : “Illustrating the trade-off between time, quality, and success probability in heuristic search”, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Prague, Czechia, pp. 1807-1812, (ACM Press) (2019)
  • C. Doerr, J. Dréo, P. Kerschke : “Making a case for (Hyper-)parameter tuning as benchmark problems”, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Prague, Czechia, pp. 1755-1764, (ACM Press) (2019)
  • N. Buskulic, C. Doerr : “Maximizing drift is not optimal for solving OneMax”, Genetic and Evolutionary Computation Conference, Companion Material, Prague, Czechia, pp. 425-426, (ACM Press) (2019)
  • A. Rodionova, K. Antonov, A. Buzdalova, C. Doerr : “Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates”, Genetic and Evolutionary Computation Conference, Prague, Czechia, pp. 855-863, (ACM Press) (2019)
  • D. Vermetten, S. Van Rijn, Th. Bäck, C. Doerr : “Online selection of CMA-ES variants”, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference, Prague, Czechia, pp. 951-959, (ACM Press) (2019)
  • B. Doerr, C. Doerr, J. Lengler : “Self-adjusting mutation rates with provably optimal success rules”, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference, Prague, Czechia, pp. 1479-1487, (ACM Press) (2019)
  • F. Ye, C. Doerr, Th. Bäck : “Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation”, 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand, pp. 2292-2299, (IEEE) (2019)
  • P. Afshani, M. Agrawal, B. Doerr, C. Doerr, K. Larsen, K. Mehlhorn : “The query complexity of a permutation-based variant of Mastermind”, Discrete Applied Mathematics, (Elsevier) (2019)
  • C. Doerr, D. Sudholt : “Preface to the Special Issue on Theory of Genetic and Evolutionary Computation”, Algorithmica, vol. 81 (2), pp. 589-592, (Springer Verlag) (2019)
  • B. Calvo, O. Shir, J. Ceberio, C. Doerr, H. Wang, Th. Bäck, J. Lozano : “Bayesian performance analysis for black-box optimization benchmarking”, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Prague, Czechia, pp. 1789-1797, (ACM Press) (2019)
  • B. Doerr, C. Doerr, F. Neumann : “Fast re-optimization via structural diversity”, The Genetic and Evolutionary Computation Conference, Proceedings of the Genetic and Evolutionary Computation Conference, Prague, Czechia, pp. 233-241, (ACM Press) (2019)
  • T. Friedrich, C. Doerr, D. Arnold : “Proceedings of the 15^th ACM/SIGEVO Conference on Foundations of Genetic Algorithms”, (ACM Press) (2019)
  • B. Doerr, C. Doerr, T. Kötzing : “Solving Problems with Unknown Solution Length at Almost No Extra Cost”, Algorithmica, vol. 81, pp. 703-748, (Springer Verlag) (2019)
• 2018
  • C. Doerr : “Optimisation inspirée par la nature”, Images des mathématiques, (CNRS) (2018)
  • C. Doerr, E. Carvalho Pinto : “A Simple Proof for the Usefulness of Crossover in Black-Box Optimization”, PPSN 2018: Parallel Problem Solving from Nature – PPSN XV, vol. 11102, Lecture Notes in Computer Science, Coimbra, Portugal, pp. 29-41, (Springer) (2018)
  • C. Doerr, M. Wagner : “Sensitivity of Parameter Control Mechanisms with Respect to Their Initialization”, International Conference on Parallel Problem Solving from Nature (PPSN 2018), vol. 11102, Lecture Notes in Computer Science, Coimbra, Portugal, pp. 360-372 (2018)
  • S. Van Rijn, C. Doerr, Th. Bäck : “Towards an Adaptive CMA-ES Configurator”, Parallel Problem Solving from Nature – PPSN XV. PPSN 2018., vol. 11101, Lecture Notes in Computer Science, Coimbra, Portugal, pp. 54-65, (Springer) (2018)
  • O. Shir, C. Doerr, Th. Bäck : “Compiling a benchmarking test-suite for combinatorial black-box optimization”, GECCO '18 - Genetic and Evolutionary Computation Conference Companion, Kyoto, Japan, pp. 1753-1760, (ACM Press) (2018)
  • A. Neumann, W. Gao, C. Doerr, F. Neumann, M. Wagner : “Discrepancy-based evolutionary diversity optimization”, GECCO '18 - Genetic and Evolutionary Computation Conference, Kyoto, France, pp. 991-998, (ACM Press) (2018)
  • C. Doerr : “Dynamic parameter choices in evolutionary computation”, GECCO '18 - Genetic and Evolutionary Computation Conference, Kyoto, Japan, pp. 800-830, (ACM Press) (2018)
  • C. Doerr, M. Wagner : “Simple on-the-fly parameter selection mechanisms for two classical discrete black-box optimization benchmark problems”, GECCO '18 - Genetic and Evolutionary Computation Conference, Kyoto, Japan, pp. 943-950, (ACM Press) (2018)
  • C. Doerr, F. Ye, S. Van Rijn, H. Wang, Th. Bäck : “Towards a theory-guided benchmarking suite for discrete black-box optimization heuristics”, GECCO '18 - Genetic and Evolutionary Computation Conference, Kyoto, France, pp. 951-958, (ACM Press) (2018)
  • B. Doerr, C. Doerr : “Optimal Static and Self-Adjusting Parameter Choices for the ( 1 + ( λ , λ ) ) Genetic Algorithm”, Algorithmica, vol. 80, pp. 1658-1709, (Springer Verlag) (2018)
  • B. Doerr, C. Doerr, M. Gnewuch : “Probabilistic Lower Discrepancy Bounds for Latin Hypercube Samples”, chapter in Contemporary Computational Mathematics - A Celebration of the 80^th Birthday of Ian Sloan, (ISBN: 978-3-319-72455-3) (2018)
  • B. Doerr, C. Doerr, T. Kötzing : “Static and Self-Adjusting Mutation Strengths for Multi-valued Decision Variables”, Algorithmica, vol. 80, pp. 1732-1768, (Springer Verlag) (2018)
  • C. Doerr, J. Lengler : “The (1+1) Elitist Black-Box Complexity of LeadingOnes”, Algorithmica, vol. 80, pp. 1579-1603, (Springer Verlag) (2018)
• 2017
  • C. Doerr, Ch. Igel, L. Thiele, X. Yao : “Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 17191)”, Dagstuhl Reports, vol. 7 (5), pp. 22-55, (Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik) (2017)
  • C. Doerr, J. Lengler : “Introducing Elitist Black-Box Models: When Does Elitist Behavior Weaken the Performance of Evolutionary Algorithms?”, Evolutionary Computation, vol. 25 (4), pp. 587-606, (Massachusetts Institute of Technology Press (MIT Press)) (2017)
  • C. Doerr, E. Carvalho Pinto : “Towards a More Practice-Aware Runtime Analysis”, EA 2017 - 13^th International Conference on Artificial Evolution, Paris, France, pp. 298-305 (2017)
  • C. Doerr : “Non-static parameter choices in evolutionary computation”, GECCO '17 - Genetic and Evolutionary Computation Conference, Berlin, Germany, pp. 736-761, (ACM Press) (2017)
  • B. Doerr, C. Doerr, T. Kötzing : “Unknown solution length problems with no asymptotically optimal run time”, Genetic and Evolutionary Computation Conference (GECCO'17), Berlin, Germany, pp. 1367-1374, (ACM) (2017)
  • C. Doerr, J. Lengler : “OneMax in Black-Box Models with Several Restrictions”, Algorithmica, vol. 78, pp. 610-640, (Springer Verlag) (2017)
  • C. Doerr, F. Chicano : “Preface to the Special Issue on Theory of Genetic and Evolutionary Computation”, Algorithmica, vol. 78 (2), pp. 558-560, (Springer Verlag) (2017)
• 2016
  • B. Doerr, C. Doerr, R. Spoehel, H. Thomas : “Playing Mastermind with Many Colors”, Journal of the ACM (JACM), vol. 63, (Association for Computing Machinery) (2016)
  • B. Doerr, C. Doerr, J. Yang : “$k$-Bit Mutation with Self-Adjusting $k$ Outperforms Standard Bit Mutation”, Parallel Problem Solving from Nature – PPSN XIV, vol. 9921, Lecture Notes in Computer Science, Edinburgh, United Kingdom, pp. 824-834 (2016)
  • B. Doerr, C. Doerr, T. Kötzing : “Provably Optimal Self-Adjusting Step Sizes for Multi-Valued Decision Variables”, Parallel Problem Solving from Nature – PPSN XIV, vol. 9921, Lecture Notes in Computer Science, Edinburgh, United Kingdom, pp. 782-791 (2016)
  • B. Doerr, C. Doerr, J. Yang : “Optimal Parameter Choices via Precise Black-Box Analysis”, GECCO 2016 - Genetic and Evolutionary Computation Conference, Denver, United States, pp. 1123-1130, (ACM) (2016)
  • C. Doerr, J. Lengler : “The (1+1) Elitist Black-Box Complexity of LeadingOnes”, GECCO 2016 - Genetic and Evolutionary Computation Conference, Denver, United States, pp. 1131-1138, (ACM) (2016)
  • B. Doerr, C. Doerr, T. Kötzing : “The Right Mutation Strength for Multi-Valued Decision Variables”, GECCO 2016 - Genetic and Evolutionary Computation Conference, Denver, United States, pp. 1115-1122, (ACM) (2016)
  • C. Doerr, B. Doerr : “The Impact of Random Initialization on the Runtime of Randomized Search Heuristics”, Algorithmica, vol. 75, pp. 529-553, (Springer Verlag) (2016)
  • B. Doerr, C. Doerr : “Theory for Non-Theoreticians (Tutorial at ACM GECCO 2016)”, (2016)
  • C. Doerr, J. Handl, E. Hart, G. Ochoa, A. Shehu, T. Tusar, A. Vostinar, Ch. Zarges, N. Zincir Heywood : “Women@GECCO 2016 Chairs’ Welcome”, (2016)
  • B. Doerr, C. Doerr, Sh. Moran, Sh. Moran : “Simple and optimal randomized fault-tolerant rumor spreading”, Distributed Computing, vol. 29 (2), pp. 89-104, (Springer Verlag) (2016)
  • A. Clementi, P. Crescenzi, C. Doerr, P. Fraigniaud, F. Pasquale, R. Silvestri : “Rumor Spreading in Random Evolving Graphs”, Random Structures and Algorithms, vol. 48 (2), pp. 290-312, (Wiley) (2016)
  • C. Doerr, N. Bredèche, E. Alba, Th. Bartz‑Beielstein, D. Brockhoff, B. Doerr, G. Eiben, M. Epitropakis, C. Fonseca, A. Guerreiro, E. Haasdijk, J. Heinerman, J. Hubert, P. Lehre, L. Malago, J. Merelo Guervós, J. Miller, B. Naujoks, P. Oliveto, S. Picek, N. Pillay, M. Preuss, P. Ryser‑Welch, G. Squillero, J. Stork, D. Sudholt, A. Tonda, D. Whitley, M. Zaefferer : “Tutorials at PPSN 2016”, PPSN 2016 - 14^th International Conference on Parallel Problem Solving from Nature, vol. 9921, Lecture Notes in Computer Science, Edinburgh, United Kingdom, pp. 1012-1022, (SPRINGER INT PUBLISHING AG), (ISBN: 978-3-319-45823-6; 978-3-319-45822-9) (2016)
• 2015
  • B. Doerr, C. Doerr, T. Kötzing : “Unbiased Black-Box Complexities of Jump Functions (Journal version)”, Evolutionary Computation, vol. 23 (4), pp. 641-670, (Massachusetts Institute of Technology Press (MIT Press)) (2015)
  • B. Doerr, C. Doerr : “A Tight Runtime Analysis of the (1+(λ, λ)) Genetic Algorithm on OneMax”, GECCO '15 - 2015 Annual Conference on Genetic and Evolutionary Computation, Madrid, Spain, pp. 1423-1430, (ACM) (2015)
  • C. Doerr, J. Lengler : “Elitist Black-Box Models: Analyzing the Impact of Elitist Selection on the Performance of Evolutionary Algorithms”, GECCO '15 - 2015 Annual Conference on Genetic and Evolutionary Computation, Madrid, Spain, pp. 839-846, (ACM) (2015)
  • A. De Perthuis de Laillevault, B. Doerr, C. Doerr : “Money for Nothing: Speeding Up Evolutionary Algorithms Through Better Initialization”, GECCO '15 - 2015 Annual Conference on Genetic and Evolutionary Computation, Madrid, Spain, pp. 815-822, (ACM) (2015)
  • C. Doerr, J. Lengler : “OneMax in Black-Box Models with Several Restrictions”, GECCO '15 - 2015 Annual Conference on Genetic and Evolutionary Computation, Madrid, Spain, pp. 1431-1438, (ACM) (2015)
  • B. Doerr, C. Doerr : “Optimal Parameter Choices Through Self-Adjustment: Applying the 1/5-th Rule in Discrete Settings”, GECCO '15 - 2015 Annual Conference on Genetic and Evolutionary Computation, Madrid, Spain, pp. 1335-1342, (ACM) (2015)
  • B. Doerr, C. Doerr, T. Kötzing : “Solving Problems with Unknown Solution Length at (Almost) No Extra Cost”, GECCO '15 - 2015 Annual Conference on Genetic and Evolutionary Computation, Madrid, Spain, pp. 831-838, (ACM) (2015)
  • B. Doerr, C. Doerr, F. Ebel : “From black-box complexity to designing new genetic algorithms”, Theoretical Computer Science, vol. 567, pp. 87-104, (Elsevier) (2015)
• 2014
  • B. Doerr, C. Doerr : “The impact of random initialization on the runtime of randomized search heuristics”, Proceedings of the 2014 conference on Genetic and evolutionary computation, Vancouver, Canada, pp. 1375-1382, (ACM) (2014)
  • B. Doerr, C. Doerr, T. Koetzing : “Unbiased black-box complexities of jump functions: how to cross large plateaus”, Proceedings of the 2014 conference on Genetic and evolutionary computation, Vancouver, Canada, pp. 769-776, (ACM) (2014)
  • B. Doerr, C. Doerr : “Black-box complexity: from complexity theory to playing Mastermind”, pp. 617-640 (2014)
  • C. Doerr, M. Gnewuch, M. Wahlström : “Calculation of Discrepancy Measures and Applications”, chapter in A Panorama of Discrepancy Theory, pp. 621-678, (Springer), (ISBN: 978-3-319-04695-2) (2014)
  • C. Doerr, Jens M. Schmidt, G. Ramakrishna : “Computing Minimum Cycle Bases in Weighted Partial 2-Trees in Linear Time”, Journal of Graph Algorithms and Applications, vol. 18, pp. 325-346, (Brown University) (2014)
  • B. Doerr, C. Doerr : “Playing Mastermind with Constant-Size Memory”, Theory of Computing Systems, vol. 55, pp. 658-684, (Springer Verlag) (2014)
  • B. Doerr, C. Doerr : “Ranking-Based Black-Box Complexity”, Algorithmica, vol. 68, pp. 571-609, (Springer Verlag) (2014)
  • B. Doerr, C. Doerr : “Reducing the arity in unbiased black-box complexity”, Theoretical Computer Science, vol. 545, pp. 108-121, (Elsevier) (2014)
  • X. Chen, B. Doerr, C. Doerr, X. Hu, M. Weidong, R. Van Stee : “The Price of Anarchy for Selfish Ring Routing is Two”, ACM Transactions on Economics and Computation, vol. 2, pp. 8, (Association for Computing Machinery (ACM)) (2014)
  • B. Doerr, C. Doerr, T. Kötzing : “The unbiased black-box complexity of partition is polynomial”, Artificial Intelligence (AIJ), vol. 216, pp. 12, (Elsevier) (2014)
  • U.‑M. O'Reilly, A. Esparcia‑Alcazar, A. Auger, C. Doerr, A. Ekart, G. Ochoa : “Women@GECCO 2014”, pp. 2 (2014)
• 2013
  • A. Clementi, P. Crescenzi, C. Doerr, P. Fraigniaud, M. Isopi, A. Panconesi, F. Pasquale, R. Silvestri : “Rumor Spreading in Random Evolving Graphs”, Algorithms – ESA 2013, vol. 8125, Lecture Notes in Computer Science, Sophia Antipolis, France, pp. 325-336, (Springer) (2013)
  • C. Doerr, Jens M. Schmidt, G. Ramakrishna : “Computing Minimum Cycle Bases in Weighted Partial 2-Trees in Linear Time”, Graph-Theoretic Concepts in Computer Science, 39^th International Workshop, WG 2013, Lübeck, Germany, June 19-21, 2013, Revised Papers, vol. 8165, Lecture Notes in Computer Science, Luebeck, Germany, pp. 225-236 (2013)
  • C. Winzen : “Direction-reversing quasi-random rumor spreading with restarts”, Information Processing Letters, vol. 113, pp. 921-926, (Elsevier) (2013)