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ORCIDCarola Doerr
Carola Winzen
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- affiliation: Sorbonne Université, CNRS, LIP6, Paris, France
- affiliation (former): Max Planck Institute for Informatics, Saarbrücken, Germany
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2020 – today
- 2022
[j37]Maxim Buzdalov
, Benjamin Doerr, Carola Doerr, Dmitry Vinokurov:
Fixed-Target Runtime Analysis. Algorithmica 84(6): 1762-1793 (2022)- [j36]François Clément, Carola Doerr, Luís Paquete:
Star discrepancy subset selection: Problem formulation and efficient approaches for low dimensions. J. Complex. 70: 101645 (2022) - [j35]Laurent Meunier, Herilalaina Rakotoarison, Pak-Kan Wong, Baptiste Rozière, Jérémy Rapin, Olivier Teytaud, Antoine Moreau, Carola Doerr:
Black-Box Optimization Revisited: Improving Algorithm Selection Wizards Through Massive Benchmarking. IEEE Trans. Evol. Comput. 26(3): 490-500 (2022) - [j34]Hao Wang, Diederick Vermetten, Furong Ye, Carola Doerr, Thomas Bäck:
IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics. ACM Trans. Evol. Learn. Optim. 2(1): 3:1-3:29 (2022) - [c99]Hao Wang, Diederick Vermetten, Furong Ye, Carola Doerr, Thomas Bäck:
IOHanalyzer: Detailed performance analyses for iterative optimization heuristics: hot-off-the-press track @ GECCO 2022. GECCO Companion 2022: 49-50 - [c98]Furong Ye, Carola Doerr, Hao Wang, Thomas Bäck:
Automated configuration of genetic algorithms by tuning for anytime performance: hot-off-the-press track at GECCCO 2022. GECCO Companion 2022: 51-52 - [c97]Gjorgjina Cenikj, Ryan Dieter Lang, Andries Petrus Engelbrecht, Carola Doerr, Peter Korosec, Tome Eftimov:
SELECTOR: selecting a representative benchmark suite for reproducible statistical comparison. GECCO 2022: 620-629 - [c96]Ana Kostovska, Diederick Vermetten, Saso Dzeroski, Carola Doerr, Peter Korosec, Tome Eftimov:
The importance of landscape features for performance prediction of modular CMA-ES variants. GECCO 2022: 648-656 - [c95]André Biedenkapp, Nguyen Dang, Martin S. Krejca, Frank Hutter, Carola Doerr:
Theory-inspired parameter control benchmarks for dynamic algorithm configuration. GECCO 2022: 766-775 - [c94]Diederick Vermetten, Hao Wang, Manuel López-Ibáñez, Carola Doerr, Thomas Bäck:
Analyzing the impact of undersampling on the benchmarking and configuration of evolutionary algorithms. GECCO 2022: 867-875 - [c93]Quentin Renau, Johann Dréo, Alain Peres, Yann Semet, Carola Doerr, Benjamin Doerr:
Automated algorithm selection for radar network configuration. GECCO 2022: 1263-1271 - [c92]Carola Doerr, Hao Wang, Diederick Vermetten, Thomas Bäck, Jacob de Nobel, Furong Ye:
Benchmarking and analyzing iterative optimization heuristics with IOH profiler. GECCO Companion 2022: 1334-1341 - [i90]André Biedenkapp, Nguyen Dang, Martin S. Krejca, Frank Hutter, Carola Doerr:
Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration. CoRR abs/2202.03259 (2022) - [i89]Furong Ye, Diederick L. Vermetten, Carola Doerr, Thomas Bäck:
Non-Elitist Selection among Survivor Configurations can Improve the Performance of Irace. CoRR abs/2203.09227 (2022) - [i88]Anja Jankovic, Diederick Vermetten, Ana Kostovska, Jacob de Nobel, Tome Eftimov, Carola Doerr:
Trajectory-based Algorithm Selection with Warm-starting. CoRR abs/2204.06397 (2022) - [i87]Dominik Schröder, Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck:
Chaining of Numerical Black-box Algorithms: Warm-Starting and Switching Points. CoRR abs/2204.06539 (2022) - [i86]Ana Kostovska, Diederick Vermetten, Saso Dzeroski, Carola Doerr, Peter Korosec, Tome Eftimov:
The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants. CoRR abs/2204.07431 (2022) - [i85]Diederick Vermetten, Hao Wang, Manuel López-Ibáñez, Carola Doerr, Thomas Bäck:
Analyzing the Impact of Undersampling on the Benchmarking and Configuration of Evolutionary Algorithms. CoRR abs/2204.09353 (2022) - [i84]Ana Kostovska, Anja Jankovic, Diederick Vermetten, Jacob de Nobel, Hao Wang, Tome Eftimov, Carola Doerr:
Per-run Algorithm Selection with Warm-starting using Trajectory-based Features. CoRR abs/2204.09483 (2022) - [i83]Gjorgjina Cenikj, Ryan Dieter Lang, Andries Petrus Engelbrecht, Carola Doerr, Peter Korosec, Tome Eftimov:
SELECTOR: Selecting a Representative Benchmark Suite for Reproducible Statistical Comparison. CoRR abs/2204.11527 (2022) - [i82]Carola Doerr, Martin S. Krejca:
Run Time Analysis for Random Local Search on Generalized Majority Functions. CoRR abs/2204.12770 (2022) - [i81]Kirill Antonov, Elena Raponi, Hao Wang, Carola Doerr:
High Dimensional Bayesian Optimization with Kernel Principal Component Analysis. CoRR abs/2204.13753 (2022) - [i80]Quentin Renau, Johann Dréo, Alain Peres, Yann Semet, Carola Doerr, Benjamin Doerr:
Automated Algorithm Selection for Radar Network Configuration. CoRR abs/2205.03670 (2022) - 2021
- [j33]Benjamin Doerr, Carola Doerr, Johannes Lengler:
Self-Adjusting Mutation Rates with Provably Optimal Success Rules. Algorithmica 83(10): 3108-3147 (2021) - [j32]Nathan Buskulic, Carola Doerr:
Maximizing Drift Is Not Optimal for Solving OneMax. Evol. Comput. 29(4): 521-541 (2021) - [c91]Kirill Antonov, Maxim Buzdalov, Arina Buzdalova, Carola Doerr:
Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms. CEC 2021: 878-885 - [c90]Quentin Renau, Johann Dréo, Carola Doerr, Benjamin Doerr:
Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions. EvoApplications 2021: 17-33 - [c89]Mohamed El Yafrani, Marcella Scoczynski Ribeiro Martins, Inkyung Sung, Markus Wagner, Carola Doerr, Peter Nielsen:
MATE: A Model-Based Algorithm Tuning Engine - A Proof of Concept Towards Transparent Feature-Dependent Parameter Tuning Using Symbolic Regression. EvoCOP 2021: 51-67 - [c88]Anja Jankovic, Tome Eftimov, Carola Doerr:
Towards Feature-Based Performance Regression Using Trajectory Data. EvoApplications 2021: 601-617 - [c87]Ana Kostovska, Diederick Vermetten, Carola Doerr, Saso Dzeroski, Pance Panov, Tome Eftimov:
OPTION: optimization algorithm benchmarking ontology. GECCO Companion 2021: 239-240 - [c86]Furong Ye, Carola Doerr, Thomas Bäck:
Leveraging benchmarking data for informed one-shot dynamic algorithm selection. GECCO Companion 2021: 245-246 - [c85]Maxim Buzdalov, Carola Doerr:
Optimal static mutation strength distributions for the (1 + λ) evolutionary algorithm on OneMax. GECCO 2021: 660-668 - [c84]Tome Eftimov, Anja Jankovic, Gorjan Popovski, Carola Doerr, Peter Korosec:
Personalizing performance regression models to black-box optimization problems. GECCO 2021: 669-677 - [c83]Anja Jankovic, Gorjan Popovski, Tome Eftimov, Carola Doerr:
The impact of hyper-parameter tuning for landscape-aware performance regression and algorithm selection. GECCO 2021: 687-696 - [c82]Amine Aziz-Alaoui, Carola Doerr, Johann Dréo:
Towards large scale automated algorithm design by integrating modular benchmarking frameworks. GECCO Companion 2021: 1365-1374 - [c81]Jacob de Nobel, Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck:
Tuning as a means of assessing the benefits of new ideas in interplay with existing algorithmic modules. GECCO Companion 2021: 1375-1384 - [i79]Carola Doerr, Luís Paquete:
Star Discrepancy Subset Selection: Problem Formulation and Efficient Approaches for Low Dimensions. CoRR abs/2101.07881 (2021) - [i78]Quentin Renau, Johann Dréo, Carola Doerr, Benjamin Doerr:
Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions. CoRR abs/2102.00736 (2021) - [i77]Maxim Buzdalov, Carola Doerr:
Optimal Static Mutation Strength Distributions for the (1+λ) Evolutionary Algorithm on OneMax. CoRR abs/2102.04944 (2021) - [i76]Anja Jankovic, Tome Eftimov, Carola Doerr:
Towards Feature-Based Performance Regression Using Trajectory Data. CoRR abs/2102.05370 (2021) - [i75]Amine Aziz-Alaoui, Carola Doerr, Johann Dréo:
Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks. CoRR abs/2102.06435 (2021) - [i74]Furong Ye, Carola Doerr, Thomas Bäck:
Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection. CoRR abs/2102.06481 (2021) - [i73]Kirill Antonov, Maxim Buzdalov, Arina Buzdalova, Carola Doerr:
Blending Dynamic Programming with Monte Carlo Simulation for Bounding the Running Time of Evolutionary Algorithms. CoRR abs/2102.11461 (2021) - [i72]Jacob de Nobel, Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck:
Tuning as a Means of Assessing the Benefits of New Ideas in Interplay with Existing Algorithmic Modules. CoRR abs/2102.12905 (2021) - [i71]Anja Jankovic, Gorjan Popovski, Tome Eftimov, Carola Doerr:
The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection. CoRR abs/2104.09272 (2021) - [i70]Tome Eftimov, Anja Jankovic, Gorjan Popovski, Carola Doerr, Peter Korosec:
Personalizing Performance Regression Models to Black-Box Optimization Problems. CoRR abs/2104.10999 (2021) - [i69]Ana Kostovska, Diederick Vermetten, Carola Doerr, Saso Dzeroski, Pance Panov, Tome Eftimov:
OPTION: OPTImization Algorithm Benchmarking ONtology. CoRR abs/2104.11889 (2021) - [i68]Furong Ye, Carola Doerr, Hao Wang, Thomas Bäck:
Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance. CoRR abs/2106.06304 (2021) - [i67]Jacob de Nobel, Furong Ye, Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck:
IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics. CoRR abs/2111.04077 (2021) - 2020
- [b2]Carola Doerr:
Theory of Iterative Optimization Heuristics: From Black-Box Complexity over Algorithm Design to Parameter Control. Sorbonne Université, France, 2020 - [j31]Carola Doerr, Furong Ye, Naama Horesh, Hao Wang, Ofer M. Shir, Thomas Bäck:
Benchmarking discrete optimization heuristics with IOHprofiler. Appl. Soft Comput. 88: 106027 (2020) - [j30]Benjamin Doerr, Carola Doerr, Jing Yang:
Optimal parameter choices via precise black-box analysis. Theor. Comput. Sci. 801: 1-34 (2020) - [c80]Benjamin Doerr, Carola Doerr, Aneta Neumann, Frank Neumann, Andrew M. Sutton:
Optimization of Chance-Constrained Submodular Functions. AAAI 2020: 1460-1467 - [c79]Diederick Vermetten, Hao Wang, Thomas Bäck, Carola Doerr:
Towards dynamic algorithm selection for numerical black-box optimization: investigating BBOB as a use case. GECCO 2020: 654-662 - [c78]Jakob Bossek, Carola Doerr, Pascal Kerschke:
Initial design strategies and their effects on sequential model-based optimization: an exploratory case study based on BBOB. GECCO 2020: 778-786 - [c77]Anja Jankovic, Carola Doerr:
Landscape-aware fixed-budget performance regression and algorithm selection for modular CMA-ES variants. GECCO 2020: 841-849 - [c76]Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck:
Integrated vs. sequential approaches for selecting and tuning CMA-ES variants. GECCO 2020: 903-912 - [c75]Gregor Papa, Carola Doerr:
Dynamic control parameter choices in evolutionary computation: GECCO 2020 tutorial. GECCO Companion 2020: 927-956 - [c74]Hao Wang, Carola Doerr, Ofer M. Shir, Thomas Bäck:
Benchmarking and analyzing iterative optimization heuristics with IOHprofiler. GECCO Companion 2020: 1043-1054 - [c73]Maxim Buzdalov, Benjamin Doerr, Carola Doerr, Dmitry Vinokurov:
Fixed-target runtime analysis. GECCO 2020: 1295-1303 - [c72]Jakob Bossek, Carola Doerr, Pascal Kerschke, Aneta Neumann, Frank Neumann:
Evolving Sampling Strategies for One-Shot Optimization Tasks. PPSN (1) 2020: 111-124 - [c71]Quentin Renau, Carola Doerr, Johann Dréo, Benjamin Doerr:
Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy. PPSN (2) 2020: 139-153 - [c70]Laurent Meunier, Carola Doerr, Jérémy Rapin, Olivier Teytaud:
Variance Reduction for Better Sampling in Continuous Domains. PPSN (1) 2020: 154-168 - [c69]Elena Raponi, Hao Wang, Mariusz Bujny, Simonetta Boria, Carola Doerr:
High Dimensional Bayesian Optimization Assisted by Principal Component Analysis. PPSN (1) 2020: 169-183 - [c68]Arina Buzdalova, Carola Doerr, Anna Rodionova:
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm. PPSN (2) 2020: 485-499 - [c67]Maxim Buzdalov, Carola Doerr:
Optimal Mutation Rates for the (1+λ ) EA on OneMax. PPSN (2) 2020: 574-587 - [c66]Furong Ye, Hao Wang, Carola Doerr, Thomas Bäck:
Benchmarking a (μ +λ ) Genetic Algorithm with Configurable Crossover Probability. PPSN (2) 2020: 699-713 - [c65]Tome Eftimov, Gorjan Popovski, Quentin Renau, Peter Korosec, Carola Doerr:
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes. SSCI 2020: 775-782 - [p3]Carola Doerr:
Complexity Theory for Discrete Black-Box Optimization Heuristics. Theory of Evolutionary Computation 2020: 133-212 - [p2]Benjamin Doerr, Carola Doerr:
Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices. Theory of Evolutionary Computation 2020: 271-321 - [e3]Thomas Bäck, Mike Preuss, André H. Deutz, Hao Wang, Carola Doerr, Michael T. M. Emmerich, Heike Trautmann:
Parallel Problem Solving from Nature - PPSN XVI - 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I. Lecture Notes in Computer Science 12269, Springer 2020, ISBN 978-3-030-58111-4 [contents] - [e2]Thomas Bäck, Mike Preuss, André H. Deutz, Hao Wang, Carola Doerr, Michael T. M. Emmerich, Heike Trautmann:
Parallel Problem Solving from Nature - PPSN XVI - 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part II. Lecture Notes in Computer Science 12270, Springer 2020, ISBN 978-3-030-58114-5 [contents] - [i66]Jakob Bossek, Carola Doerr, Pascal Kerschke:
Initial Design Strategies and their Effects on Sequential Model-Based Optimization. CoRR abs/2003.13826 (2020) - [i65]Maxim Buzdalov, Benjamin Doerr, Carola Doerr, Dmitry Vinokurov:
Fixed-Target Runtime Analysis. CoRR abs/2004.09613 (2020) - [i64]Laurent Meunier, Carola Doerr, Jérémy Rapin, Olivier Teytaud:
Variance Reduction for Better Sampling in Continuous Domains. CoRR abs/2004.11687 (2020) - [i63]Mohamed El Yafrani, Marcella Scoczynski Ribeiro Martins, Inkyung Sung, Markus Wagner, Carola Doerr, Peter Nielsen:
MATE: A Model-based Algorithm Tuning Engine. CoRR abs/2004.12750 (2020) - [i62]Furong Ye, Hao Wang, Carola Doerr, Thomas Bäck:
Benchmarking a $(μ+λ)$ Genetic Algorithm with Configurable Crossover Probability. CoRR abs/2006.05889 (2020) - [i61]Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck:
Towards Dynamic Algorithm Selection for Numerical Black-Box Optimization: Investigating BBOB as a Use Case. CoRR abs/2006.06586 (2020) - [i60]Anja Jankovic, Carola Doerr:
Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants. CoRR abs/2006.09855 (2020) - [i59]Arina Buzdalova, Carola Doerr, Anna Rodionova:
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm. CoRR abs/2006.11026 (2020) - [i58]Quentin Renau, Carola Doerr, Johann Dréo, Benjamin Doerr:
Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy. CoRR abs/2006.11135 (2020) - [i57]Maxim Buzdalov, Carola Doerr:
Optimal Mutation Rates for the (1+λ) EA on OneMax. CoRR abs/2006.11457 (2020) - [i56]Elena Raponi, Hao Wang, Mariusz Bujny, Simonetta Boria, Carola Doerr:
High Dimensional Bayesian Optimization Assisted by Principal Component Analysis. CoRR abs/2007.00925 (2020) - [i55]Thomas Bartz-Beielstein, Carola Doerr, Jakob Bossek, Sowmya Chandrasekaran, Tome Eftimov, Andreas Fischbach, Pascal Kerschke, Manuel López-Ibáñez, Katherine M. Malan, Jason H. Moore, Boris Naujoks, Patryk Orzechowski, Vanessa Volz, Markus Wagner, Thomas Weise:
Benchmarking in Optimization: Best Practice and Open Issues. CoRR abs/2007.03488 (2020) - [i54]Hao Wang, Diederick Vermetten, Furong Ye, Carola Doerr, Thomas Bäck:
IOHanalyzer: Performance Analysis for Iterative Optimization Heuristic. CoRR abs/2007.03953 (2020) - [i53]Tome Eftimov, Gorjan Popovski, Quentin Renau, Peter Korosec, Carola Doerr:
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes. CoRR abs/2009.14506 (2020) - [i52]Laurent Meunier, Herilalaina Rakotoarison, Pak-Kan Wong, Baptiste Rozière, Jérémy Rapin, Olivier Teytaud, Antoine Moreau, Carola Doerr:
Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking. CoRR abs/2010.04542 (2020) - [i51]Noor H. Awad, Gresa Shala, Difan Deng, Neeratyoy Mallik, Matthias Feurer, Katharina Eggensperger, André Biedenkapp, Diederick Vermetten, Hao Wang, Carola Doerr, Marius Lindauer, Frank Hutter:
Squirrel: A Switching Hyperparameter Optimizer. CoRR abs/2012.08180 (2020)
2010 – 2019
- 2019
- [j29]Carola Doerr, Dirk Sudholt:
Preface to the Special Issue on Theory of Genetic and Evolutionary Computation. Algorithmica 81(2): 589-592 (2019) - [j28]Benjamin Doerr, Carola Doerr, Timo Kötzing:
Solving Problems with Unknown Solution Length at Almost No Extra Cost. Algorithmica 81(2): 703-748 (2019) - [j27]Peyman Afshani, Manindra Agrawal, Benjamin Doerr, Carola Doerr, Kasper Green Larsen, Kurt Mehlhorn:
The query complexity of a permutation-based variant of Mastermind. Discret. Appl. Math. 260: 28-50 (2019) - [c64]Furong Ye, Carola Doerr, Thomas Bäck:
Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation. CEC 2019: 2292-2299 - [c63]Benjamin Doerr, Carola Doerr, Frank Neumann:
Fast re-optimization via structural diversity. GECCO 2019: 233-241 - [c62]Nathan Buskulic, Carola Doerr:
Maximizing drift is not optimal for solving OneMax. GECCO (Companion) 2019: 425-426 - [c61]Anna Rodionova, Kirill Antonov, Arina Buzdalova, Carola Doerr:
Offspring population size matters when comparing evolutionary algorithms with self-adjusting mutation rates. GECCO 2019: 855-863 - [c60]Nguyen Dang, Carola Doerr:
Hyper-parameter tuning for the (1 + (λ, λ)) GA. GECCO 2019: 889-897 - [c59]Carola Doerr:
Dynamic parameter choices in evolutionary computation. GECCO (Companion) 2019: 890-922 - [c58]Diederick Vermetten, Sander van Rijn, Thomas Bäck, Carola Doerr:
Online selection of CMA-ES variants. GECCO 2019: 951-959 - [c57]Johann Dréo, Carola Doerr, Yann Semet:
Coupling the design of benchmark with algorithm in landscape-aware solver design. GECCO (Companion) 2019: 1419-1420 - [c56]Benjamin Doerr, Carola Doerr, Johannes Lengler:
Self-adjusting mutation rates with provably optimal success rules. GECCO 2019: 1479-1487 - [c55]Carola Doerr, Johann Dréo, Pascal Kerschke:
Making a case for (Hyper-)parameter tuning as benchmark problems. GECCO (Companion) 2019: 1755-1764 - [c54]Borja Calvo, Ofer M. Shir, Josu Ceberio, Carola Doerr, Hao Wang, Thomas Bäck, José Antonio Lozano:
Bayesian performance analysis for black-box optimization benchmarking. GECCO (Companion) 2019: 1789-1797 - [c53]Carola Doerr, Furong Ye, Naama Horesh, Hao Wang, Ofer M. Shir, Thomas Bäck:
Benchmarking discrete optimization heuristics with IOHprofiler. GECCO (Companion) 2019: 1798-1806 - [c52]Ivan Ignashov, Arina Buzdalova, Maxim Buzdalov, Carola Doerr:
Illustrating the trade-off between time, quality, and success probability in heuristic search: a discussion paper. GECCO (Companion) 2019: 1807-1812 - [c51]Anja Jankovic, Carola Doerr:
Adaptive landscape analysis. GECCO (Companion) 2019: 2032-2035 - [c50]Quentin Renau, Johann Dréo, Carola Doerr, Benjamin Doerr:
Expressiveness and robustness of landscape features. GECCO (Companion) 2019: 2048-2051 - [c49]Dmitry Vinokurov, Maxim Buzdalov, Arina Buzdalova, Benjamin Doerr, Carola Doerr:
Fixed-target runtime analysis of the (1 + 1) EA with resampling. GECCO (Companion) 2019: 2068-2071 - [e1]Tobias Friedrich, Carola Doerr, Dirk V. Arnold:
Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, FOGA 2019, Potsdam, Germany, August 27-29, 2019. ACM 2019, ISBN 978-1-4503-6254-2 [contents] - [i50]Furong Ye, Carola Doerr, Thomas Bäck:
Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation. CoRR abs/1901.05573 (2019) - [i49]Benjamin Doerr, Carola Doerr, Frank Neumann:
Fast Re-Optimization via Structural Diversity. CoRR abs/1902.00304 (2019) - [i48]Benjamin Doerr, Carola Doerr, Johannes Lengler:
Self-Adjusting Mutation Rates with Provably Optimal Success Rules. CoRR abs/1902.02588 (2019) - [i47]Nguyen Dang, Carola Doerr:
Hyper-Parameter Tuning for the (1+(λ, λ)) GA. CoRR abs/1904.04608 (2019) - [i46]Diederick Vermetten, Sander van Rijn, Thomas Bäck, Carola Doerr:
Online Selection of CMA-ES Variants. CoRR abs/1904.07801 (2019) - [i45]Nathan Buskulic, Carola Doerr:
Maximizing Drift is Not Optimal for Solving OneMax. CoRR abs/1904.07818 (2019) - [i44]Anna Rodionova, Kirill Antonov, Arina Buzdalova, Carola Doerr:
Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates. CoRR abs/1904.08032 (2019) - [i43]Benjamin Doerr, Carola Doerr, Aneta Neumann, Frank Neumann, Andrew M. Sutton:
Optimization of Chance-Constrained Submodular Functions. CoRR abs/1911.11451 (2019) - [i42]Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck:
Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMA-ES. CoRR abs/1912.05899 (2019) - [i41]Jakob Bossek, Pascal Kerschke, Aneta Neumann, Frank Neumann, Carola Doerr:
One-Shot Decision-Making with and without Surrogates. CoRR abs/1912.08956 (2019) - [i40]Carola Doerr, Furong Ye, Naama Horesh, Hao Wang, Ofer M. Shir, Thomas Bäck:
Benchmarking Discrete Optimization Heuristics with IOHprofiler. CoRR abs/1912.09237 (2019) - [i39]Carola Doerr, Carlos M. Fonseca, Tobias Friedrich, Xin Yao:
Theory of Randomized Optimization Heuristics (Dagstuhl Reports 19431). Dagstuhl Reports 9(10): 61-94 (2019) - 2018
- [j26]Carola Doerr, Johannes Lengler:
The (1+1) Elitist Black-Box Complexity of LeadingOnes. Algorithmica 80(5): 1579-1603 (2018) - [j25]Benjamin Doerr, Carola Doerr:
Optimal Static and Self-Adjusting Parameter Choices for the (1+(λ, λ)) Genetic Algorithm. Algorithmica 80(5): 1658-1709 (2018) - [j24]Benjamin Doerr, Carola Doerr, Timo Kötzing:
Static and Self-Adjusting Mutation Strengths for Multi-valued Decision Variables. Algorithmica 80(5):