Stop the war!Остановите войну!
for scientists:
Google
Google Scholar
Semantic Scholar
IA Scholar
CiteSeerX
ORCIDCarola Doerr
Carola Winzen
Person information
- affiliation: Sorbonne Université, CNRS, LIP6, Paris, France
- affiliation (former): Max Planck Institute for Informatics, Saarbrücken, Germany
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2022
[j34]François Clément, Carola Doerr, Luís Paquete
:
Star discrepancy subset selection: Problem formulation and efficient approaches for low dimensions. J. Complex. 70: 101645 (2022)- [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 - [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): 1732-1768 (2018) - [c48]Carola Doerr:
Dynamic parameter choices in evolutionary computation. GECCO (Companion) 2018: 800-830 - [c47]Carola Doerr, Markus Wagner:
Simple on-the-fly parameter selection mechanisms for two classical discrete black-box optimization benchmark problems. GECCO 2018: 943-950 - [c46]Carola Doerr, Furong Ye, Sander van Rijn, Hao Wang, Thomas Bäck:
Towards a theory-guided benchmarking suite for discrete black-box optimization heuristics: profiling (1 + λ) EA variants on onemax and leadingones. GECCO 2018: 951-958 - [c45]Aneta Neumann, Wanru Gao, Carola Doerr, Frank Neumann, Markus Wagner:
Discrepancy-based evolutionary diversity optimization. GECCO 2018: 991-998 - [c44]Ofer M. Shir, Carola Doerr, Thomas Bäck:
Compiling a benchmarking test-suite for combinatorial black-box optimization: a position paper. GECCO (Companion) 2018: 1753-1760 - [c43]Eduardo Carvalho Pinto, Carola Doerr:
A Simple Proof for the Usefulness of Crossover in Black-Box Optimization. PPSN (2) 2018: 29-41 - [c42]Sander van Rijn, Carola Doerr, Thomas Bäck:
Towards an Adaptive CMA-ES Configurator. PPSN (1) 2018: 54-65 - [c41]Carola Doerr, Markus Wagner:
Sensitivity of Parameter Control Mechanisms with Respect to Their Initialization. PPSN (2) 2018: 360-372 - [c40]Gisele Lobo Pappa, Michael T. M. Emmerich, Ana L. C. Bazzan, Will N. Browne, Kalyanmoy Deb, Carola Doerr, Marko Durasevic, Michael G. Epitropakis, Saemundur O. Haraldsson, Domagoj Jakobovic, Pascal Kerschke, Krzysztof Krawiec, Per Kristian Lehre, Xiaodong Li, Andrei Lissovoi, Pekka Malo, Luis Martí, Yi Mei, Juan Julián Merelo Guervós, Julian F. Miller, Alberto Moraglio, Antonio J. Nebro, Su Nguyen, Gabriela Ochoa, Pietro S. Oliveto, Stjepan Picek, Nelishia Pillay, Mike Preuss, Marc Schoenauer, Roman Senkerik, Ankur Sinha, Ofer M. Shir, Dirk Sudholt, L. Darrell Whitley, Mark Wineberg, John R. Woodward, Mengjie Zhang:
Tutorials at PPSN 2018. PPSN (2) 2018: 477-489 - [i38]Carola Doerr:
Complexity Theory for Discrete Black-Box Optimization Heuristics. CoRR abs/1801.02037 (2018) - [i37]Aneta Neumann, Wanru Gao, Carola Doerr, Frank Neumann, Markus Wagner:
Discrepancy-based Evolutionary Diversity Optimization. CoRR abs/1802.05448 (2018) - [i36]Carola Doerr, Markus Wagner:
On the Effectiveness of Simple Success-Based Parameter Selection Mechanisms for Two Classical Discrete Black-Box Optimization Benchmark Problems. CoRR abs/1803.01425 (2018) - [i35]Benjamin Doerr, Carola Doerr:
Theory of Parameter Control for Discrete Black-Box Optimization: Provable Performance Gains Through Dynamic Parameter Choices. CoRR abs/1804.05650 (2018) - [i34]