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ORCIDBenjamin Doerr
Person information
- affiliation: École Polytechnique de Paris, Computer Science Laboratory (LIX), France
- affiliation: Saarland University, Department of Computer Science, Saarbrücken, Germany
- affiliation: Max Planck Institute for Informatics, Saarbrücken, Germany
- affiliation (PhD 2000): University of Kiel, Germany
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2020 – today
- 2024
[j132]Henry Bambury, Antoine Bultel, Benjamin Doerr
:
An Extended Jump Functions Benchmark for the Analysis of Randomized Search Heuristics. Algorithmica 86(1): 1-32 (2024)- [j131]Benjamin Doerr, Amirhossein Rajabi, Carsten Witt:
Simulated Annealing is a Polynomial-Time Approximation Scheme for the Minimum Spanning Tree Problem. Algorithmica 86(1): 64-89 (2024) - [j130]Benjamin Doerr, Yassine Ghannane, Marouane Ibn Brahim:
Runtime Analysis for Permutation-based Evolutionary Algorithms. Algorithmica 86(1): 90-129 (2024) - [j129]Benjamin Doerr, Timo Kötzing:
Lower Bounds from Fitness Levels Made Easy. Algorithmica 86(2): 367-395 (2024) - [j128]Denis Antipov, Maxim Buzdalov, Benjamin Doerr:
Lazy Parameter Tuning and Control: Choosing All Parameters Randomly from a Power-Law Distribution. Algorithmica 86(2): 442-484 (2024) - [j127]Benjamin Doerr, Andrew James Kelley:
Fourier Analysis Meets Runtime Analysis: Precise Runtimes on Plateaus. Algorithmica 86(8): 2479-2518 (2024) - [j126]Shouda Wang, Weijie Zheng, Benjamin Doerr:
Choosing the right algorithm with hints from complexity theory. Inf. Comput. 296: 105125 (2024) - [j125]Firas Ben Jedidia, Benjamin Doerr, Martin S. Krejca:
Estimation-of-distribution algorithms for multi-valued decision variables. Theor. Comput. Sci. 1003: 114622 (2024) - [c211]Benjamin Doerr, Aymen Echarghaoui, Mohammed Jamal, Martin S. Krejca:
Runtime Analysis of the (μ + 1) GA: Provable Speed-Ups from Strong Drift towards Diverse Populations. AAAI 2024: 20683-20691 - [c210]Weijie Zheng, Benjamin Doerr:
Runtime Analysis of the SMS-EMOA for Many-Objective Optimization. AAAI 2024: 20874-20882 - [c209]Weijie Zheng, Mingfeng Li, Renzhong Deng, Benjamin Doerr:
How to Use the Metropolis Algorithm for Multi-Objective Optimization? AAAI 2024: 20883-20891 - [c208]Denis Antipov, Benjamin Doerr, Alexandra Ivanova:
Already Moderate Population Sizes Provably Yield Strong Robustness to Noise. GECCO 2024 - [c207]Sacha Cerf, Benjamin Doerr, Benjamin Hebras, Yakob Kahane, Simon Wietheger:
Hot off the Press: The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem. GECCO Companion 2024: 27-28 - [c206]Matthieu Dinot, Benjamin Doerr, Ulysse Hennebelle, Sebastian Will:
Hot off the Press: Runtime Analyses of Multi-Objective Evolutionary Algorithms in the Presence of Noise. GECCO Companion 2024: 33-34 - [c205]Benjamin Doerr, Aymen Echarghaoui, Mohammed Jamal, Martin S. Krejca:
Runtime Analysis of the (μ + 1) GA: Provable Speed-Ups from Strong Drift towards Diverse Populations. GECCO Companion 2024: 35-36 - [c204]Benjamin Doerr, Joshua D. Knowles, Aneta Neumann, Frank Neumann:
A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical Analysis. GECCO 2024 - [c203]Simon Wietheger, Benjamin Doerr:
A Mathematical Runtime Analysis of the Non-dominated Sorting Genetic Algorithm III (NSGA-III). GECCO Companion 2024: 63-64 - [c202]Weijie Zheng, Benjamin Doerr:
Hot off the Press: Runtime Analysis for the NSGA-II: Proving, Quantifying, and Explaining the Inefficiency For Many Objectives. GECCO Companion 2024: 67-68 - [c201]Weijie Zheng, Benjamin Doerr:
Hot off the Press: Runtime Analysis of the SMS-EMOA for Many-Objective Optimization. GECCO Companion 2024: 69-70 - [c200]Weijie Zheng, Mingfeng Li, Renzhong Deng, Benjamin Doerr:
How to Use the Metropolis Algorithm for Multi-Objective Optimization? GECCO Companion 2024: 71-72 - [c199]Benjamin Doerr:
A Gentle Introduction to Theory (for Non-Theoreticians). GECCO Companion 2024: 800-829 - [c198]Benjamin Doerr, Martin S. Krejca, Nguyen Vu:
Superior Genetic Algorithms for the Target Set Selection Problem Based on Power-Law Parameter Choices and Simple Greedy Heuristics. GECCO 2024 - [i129]Benjamin Doerr, Andrew James Kelley:
The Runtime of Random Local Search on the Generalized Needle Problem. CoRR abs/2403.08153 (2024) - [i128]Denis Antipov, Benjamin Doerr, Alexandra Ivanova:
Already Moderate Population Sizes Provably Yield Strong Robustness to Noise. CoRR abs/2404.02090 (2024) - [i127]Benjamin Doerr, Joshua D. Knowles, Aneta Neumann, Frank Neumann:
A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical Analysis. CoRR abs/2404.03838 (2024) - [i126]Benjamin Doerr, Martin S. Krejca, Nguyen Vu:
Superior Genetic Algorithms for the Target Set Selection Problem Based on Power-Law Parameter Choices and Simple Greedy Heuristics. CoRR abs/2404.04018 (2024) - [i125]Simon Wietheger, Benjamin Doerr:
Near-Tight Runtime Guarantees for Many-Objective Evolutionary Algorithms. CoRR abs/2404.12746 (2024) - [i124]Benjamin Doerr, Martin S. Krejca, Noé Weeks:
Proven Runtime Guarantees for How the MOEA/D Computes the Pareto Front From the Subproblem Solutions. CoRR abs/2405.01014 (2024) - [i123]Benjamin Doerr, Johannes F. Lutzeyer:
Hyper-Heuristics Can Profit From Global Variation Operators. CoRR abs/2407.14237 (2024) - [i122]Weijie Zheng, Benjamin Doerr:
Overcome the Difficulties of NSGA-II via Truthful Crowding Distance with Theoretical Guarantees. CoRR abs/2407.17687 (2024) - 2023
- [j124]Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto:
(1+1) genetic programming with functionally complete instruction sets can evolve Boolean conjunctions and disjunctions with arbitrarily small error. Artif. Intell. 319: 103906 (2023) - [j123]Weijie Zheng, Benjamin Doerr:
Mathematical runtime analysis for the non-dominated sorting genetic algorithm II (NSGA-II). Artif. Intell. 325: 104016 (2023) - [j122]Weijie Zheng, Benjamin Doerr:
Theoretical Analyses of Multiobjective Evolutionary Algorithms on Multimodal Objectives. Evol. Comput. 31(4): 337-373 (2023) - [j121]Weijie Zheng, Benjamin Doerr:
From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms. J. Mach. Learn. Res. 24: 292:1-292:40 (2023) - [j120]Benjamin Doerr, Amirhossein Rajabi:
Stagnation detection meets fast mutation. Theor. Comput. Sci. 946: 113670 (2023) - [j119]Benjamin Doerr, Martin S. Krejca:
Bivariate estimation-of-distribution algorithms can find an exponential number of optima. Theor. Comput. Sci. 971: 114074 (2023) - [j118]Benjamin Doerr, Zhongdi Qu:
A First Runtime Analysis of the NSGA-II on a Multimodal Problem. IEEE Trans. Evol. Comput. 27(5): 1288-1297 (2023) - [c197]Benjamin Doerr, Zhongdi Qu:
Runtime Analysis for the NSGA-II: Provable Speed-Ups from Crossover. AAAI 2023: 12399-12407 - [c196]Benjamin Doerr, Zhongdi Qu:
From Understanding the Population Dynamics of the NSGA-II to the First Proven Lower Bounds. AAAI 2023: 12408-12416 - [c195]Benjamin Doerr, Zhongdi Qu:
Hot off the Press: A First Runtime Analysis of the NSGA-II on a Multimodal Problem. GECCO Companion 2023: 15-16 - [c194]Benjamin Doerr, Zhongdi Qu:
Hot off the Press: From Understanding the Population Dynamics of the NSGA-II to the First Proven Lower Bounds. GECCO Companion 2023: 17-18 - [c193]Benjamin Doerr, Zhongdi Qu:
Hot off the Press: Runtime Analysis for the NSGA-II - Provable Speed-Ups From Crossover. GECCO Companion 2023: 19-20 - [c192]Firas Ben Jedidia, Benjamin Doerr, Martin S. Krejca:
Estimation-of-Distribution Algorithms for Multi-Valued Decision Variables. GECCO 2023: 230-238 - [c191]Alexandra Ivanova, Denis Antipov, Benjamin Doerr:
Larger Offspring Populations Help the (1 + (λ, λlambda)) Genetic Algorithm to Overcome the Noise. GECCO 2023: 919-928 - [c190]Benjamin Doerr:
A Gentle Introduction to Theory (for Non-Theoreticians). GECCO Companion 2023: 946-975 - [c189]Benjamin Doerr, Arthur Dremaux, Johannes F. Lutzeyer, Aurélien Stumpf:
How the Move Acceptance Hyper-Heuristic Copes With Local Optima: Drastic Differences Between Jumps and Cliffs. GECCO 2023: 990-999 - [c188]Benjamin Doerr, Taha El Ghazi El Houssaini, Amirhossein Rajabi, Carsten Witt:
How Well Does the Metropolis Algorithm Cope With Local Optima? GECCO 2023: 1000-1008 - [c187]Benjamin Doerr, Andrew James Kelley:
Fourier Analysis Meets Runtime Analysis: Precise Runtimes on Plateaus. GECCO 2023: 1555-1564 - [c186]Sacha Cerf, Benjamin Doerr, Benjamin Hebras, Yakob Kahane, Simon Wietheger:
The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem. IJCAI 2023: 5522-5530 - [c185]Matthieu Dinot, Benjamin Doerr, Ulysse Hennebelle, Sebastian Will:
Runtime Analyses of Multi-Objective Evolutionary Algorithms in the Presence of Noise. IJCAI 2023: 5549-5557 - [c184]Simon Wietheger, Benjamin Doerr:
A Mathematical Runtime Analysis of the Non-dominated Sorting Genetic Algorithm III (NSGA-III). IJCAI 2023: 5657-5665 - [i121]Benjamin Doerr, Andrew James Kelley:
Fourier Analysis Meets Runtime Analysis: Precise Runtimes on Plateaus. CoRR abs/2302.08021 (2023) - [i120]Benjamin Doerr, Aymen Echarghaoui, Mohammed Jamal, Martin S. Krejca:
Lasting Diversity and Superior Runtime Guarantees for the (μ+1) Genetic Algorithm. CoRR abs/2302.12570 (2023) - [i119]Firas Ben Jedidia, Benjamin Doerr, Martin S. Krejca:
Estimation-of-Distribution Algorithms for Multi-Valued Decision Variables. CoRR abs/2302.14420 (2023) - [i118]Benjamin Doerr, Andrei Lissovoi, Pietro S. Oliveto:
(1+1) Genetic Programming With Functionally Complete Instruction Sets Can Evolve Boolean Conjunctions and Disjunctions with Arbitrarily Small Error. CoRR abs/2303.07455 (2023) - [i117]Benjamin Doerr, Anatolii Kostrygin:
Randomized Rumor Spreading Revisited (Long Version). CoRR abs/2303.11150 (2023) - [i116]Benjamin Doerr, Arthur Dremaux, Johannes F. Lutzeyer, Aurélien Stumpf:
How the Move Acceptance Hyper-Heuristic Copes With Local Optima: Drastic Differences Between Jumps and Cliffs. CoRR abs/2304.10414 (2023) - [i115]Benjamin Doerr, Taha El Ghazi El Houssaini, Amirhossein Rajabi, Carsten Witt:
How Well Does the Metropolis Algorithm Cope With Local Optima? CoRR abs/2304.10848 (2023) - [i114]Alexandra Ivanova, Denis Antipov, Benjamin Doerr:
Larger Offspring Populations Help the (1 + (λ, λ)) Genetic Algorithm to Overcome the Noise. CoRR abs/2305.04553 (2023) - [i113]Matthieu Dinot, Benjamin Doerr, Ulysse Hennebelle, Sebastian Will:
Runtime Analyses of Multi-Objective Evolutionary Algorithms in the Presence of Noise. CoRR abs/2305.10259 (2023) - [i112]Sacha Cerf, Benjamin Doerr, Benjamin Hebras, Yakob Kahane, Simon Wietheger:
The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem. CoRR abs/2305.13459 (2023) - [i111]Benjamin Doerr, Martin S. Krejca:
Bivariate Estimation-of-Distribution Algorithms Can Find an Exponential Number of Optima. CoRR abs/2310.04042 (2023) - [i110]Weijie Zheng, Benjamin Doerr:
Runtime Analysis of the SMS-EMOA for Many-Objective Optimization. CoRR abs/2312.10290 (2023) - 2022
- [j117]Denis Antipov, Benjamin Doerr, Vitalii Karavaev:
A Rigorous Runtime Analysis of the (1 + (λ , λ )) GA on Jump Functions. Algorithmica 84(6): 1573-1602 (2022) - [j116]Benjamin Doerr:
Does Comma Selection Help to Cope with Local Optima? Algorithmica 84(6): 1659-1693 (2022) - [j115]Denis Antipov, Maxim Buzdalov, Benjamin Doerr:
Fast Mutation in Crossover-Based Algorithms. Algorithmica 84(6): 1724-1761 (2022) - [j114]Maxim Buzdalov, Benjamin Doerr, Carola Doerr, Dmitry Vinokurov:
Fixed-Target Runtime Analysis. Algorithmica 84(6): 1762-1793 (2022) - [j113]Benjamin Doerr:
A sharp discrepancy bound for jittered sampling. Math. Comput. 91(336): 1871-1892 (2022) - [c183]Weijie Zheng, Yufei Liu, Benjamin Doerr:
A First Mathematical Runtime Analysis of the Non-dominated Sorting Genetic Algorithm II (NSGA-II). AAAI 2022: 10408-10416 - [c182]Benjamin Doerr, Amirhossein Rajabi:
Stagnation Detection Meets Fast Mutation. EvoCOP 2022: 191-207 - [c181]Denis Antipov, Benjamin Doerr:
Precise runtime analysis for plateau functions: (hot-off-the-press track at GECCO 2022). GECCO Companion 2022: 13-14 - [c180]Shouda Wang, Weijie Zheng, Benjamin Doerr:
Choosing the right algorithm with hints from complexity theory: (hot-off-the-press track at GECCO 2022). GECCO Companion 2022: 45-46 - [c179]Weijie Zheng, Yufei Liu, Benjamin Doerr:
A first mathematical runtime analysis of the non-dominated sorting genetic algorithm II (NSGA-II): (hot-off-the-press track at GECCO 2022). GECCO Companion 2022: 53-54 - [c178]Benjamin Doerr, Omar El Hadri, Adrien Pinard:
The (1 + (λ, λ)) global SEMO algorithm. GECCO 2022: 520-528 - [c177]Weijie Zheng, Benjamin Doerr:
Better approximation guarantees for the NSGA-II by using the current crowding distance. GECCO 2022: 611-619 - [c176]Benjamin Doerr:
A gentle introduction to theory (for non-theoreticians). GECCO Companion 2022: 890-921 - [c175]Quentin Renau, Johann Dréo, Alain Peres, Yann Semet, Carola Doerr, Benjamin Doerr:
Automated algorithm selection for radar network configuration. GECCO 2022: 1263-1271 - [c174]Benjamin Doerr, Amirhossein Rajabi, Carsten Witt:
Simulated annealing is a polynomial-time approximation scheme for the minimum spanning tree problem. GECCO 2022: 1381-1389 - [c173]Benjamin Doerr, Yassine Ghannane, Marouane Ibn Brahim:
Towards a stronger theory for permutation-based evolutionary algorithms. GECCO 2022: 1390-1398 - [c172]Benjamin Doerr, Zhongdi Qu:
A First Runtime Analysis of the NSGA-II on a Multimodal Problem. PPSN (2) 2022: 399-412 - [c171]Benjamin Doerr, Marc Dufay:
General Univariate Estimation-of-Distribution Algorithms. PPSN (2) 2022: 470-484 - [i109]Benjamin Doerr, Amirhossein Rajabi:
Stagnation Detection meets Fast Mutation. CoRR abs/2201.12158 (2022) - [i108]Weijie Zheng, Benjamin Doerr:
Better Approximation Guarantees for the NSGA-II by Using the Current Crowding Distance. CoRR abs/2203.02693 (2022) - [i107]Benjamin Doerr, Amirhossein Rajabi, Carsten Witt:
Simulated Annealing is a Polynomial-Time Approximation Scheme for the Minimum Spanning Tree Problem. CoRR abs/2204.02097 (2022) - [i106]Benjamin Doerr, Yassine Ghannane, Marouane Ibn Brahim:
Towards a Stronger Theory for Permutation-based Evolutionary Algorithms. CoRR abs/2204.07637 (2022) - [i105]Zhongdi Qu, Benjamin Doerr:
A First Runtime Analysis of the NSGA-II on a Multimodal Problem. CoRR abs/2204.13750 (2022) - [i104]Quentin Renau, Johann Dréo, Alain Peres, Yann Semet, Carola Doerr, Benjamin Doerr:
Automated Algorithm Selection for Radar Network Configuration. CoRR abs/2205.03670 (2022) - [i103]Weijie Zheng, Benjamin Doerr:
From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms. CoRR abs/2206.09090 (2022) - [i102]Benjamin Doerr, Marc Dufay:
General Univariate Estimation-of-Distribution Algorithms. CoRR abs/2206.11198 (2022) - [i101]Benjamin Doerr, Yassine Ghannane, Marouane Ibn Brahim:
Runtime Analysis for Permutation-based Evolutionary Algorithms. CoRR abs/2207.04045 (2022) - [i100]Benjamin Doerr, Zhongdi Qu:
The First Mathematical Proof That Crossover Gives Super-Constant Performance Gains For the NSGA-II. CoRR abs/2208.08759 (2022) - [i99]Benjamin Doerr, Zhongdi Qu:
From Understanding the Population Dynamics of the NSGA-II to the First Proven Lower Bounds. CoRR abs/2209.13974 (2022) - [i98]Benjamin Doerr, Omar El Hadri, Adrien Pinard:
The $(1+(λ, λ))$ Global SEMO Algorithm. CoRR abs/2210.03618 (2022) - [i97]Benjamin Doerr, Simon Wietheger:
A Mathematical Runtime Analysis of the Non-dominated Sorting Genetic Algorithm III (NSGA-III). CoRR abs/2211.08202 (2022) - [i96]Weijie Zheng, Benjamin Doerr:
Runtime Analysis for the NSGA-II: Proving, Quantifying, and Explaining the Inefficiency For Three or More Objectives. CoRR abs/2211.13084 (2022) - [i95]Josu Ceberio Uribe, Benjamin Doerr, Carsten Witt, Vicente P. Soloviev:
Estimation-of-Distribution Algorithms: Theory and Applications (Dagstuhl Seminar 22182). Dagstuhl Reports 12(5): 17-36 (2022) - 2021
- [j112]Benjamin Doerr, Carsten Witt, Jing Yang:
Runtime Analysis for Self-adaptive Mutation Rates. Algorithmica 83(4): 1012-1053 (2021) - [j111]Denis Antipov, Benjamin Doerr:
A Tight Runtime Analysis for the (μ + λ ) EA. Algorithmica 83(4): 1054-1095 (2021) - [j110]Benjamin Doerr, Timo Kötzing:
Multiplicative Up-Drift. Algorithmica 83(10): 3017-3058 (2021) - [j109]Benjamin Doerr:
The Runtime of the Compact Genetic Algorithm on Jump Functions. Algorithmica 83(10): 3059-3107 (2021) - [j108]Benjamin Doerr, Carola Doerr, Johannes Lengler:
Self-Adjusting Mutation Rates with Provably Optimal Success Rules. Algorithmica 83(10): 3108-3147 (2021) - [j107]Benjamin Doerr:
Lower Bounds for Non-Elitist Evolutionary Algorithms via Negative Multiplicative Drift. Evol. Comput. 29(2): 305-329 (2021) - [j106]Benjamin Doerr, Martin S. Krejca:
The Univariate Marginal Distribution Algorithm Copes Well with Deception and Epistasis. Evol. Comput. 29(4): 543-563 (2021) - [j105]Benjamin Doerr:
Runtime analysis of evolutionary algorithms via symmetry arguments. Inf. Process. Lett. 166: 106064 (2021) - [j104]Benjamin Doerr, Sebastian Mayer:
The recovery of ridge functions on the hypercube suffers from the curse of dimensionality. J. Complex. 63: 101521 (2021) - [j103]Benjamin Doerr, Michael Gnewuch:
On negative dependence properties of Latin hypercube samples and scrambled nets. J. Complex. 67: 101589 (2021) - [j102]Benjamin Doerr:
Exponential upper bounds for the runtime of randomized search heuristics. Theor. Comput. Sci. 851: 24-38 (2021) - [j101]Benjamin Doerr, Martin S. Krejca:
A simplified run time analysis of the univariate marginal distribution algorithm on LeadingOnes. Theor. Comput. Sci. 851: 121-128 (2021) - [j100]Denis Antipov, Benjamin Doerr:
Precise Runtime Analysis for Plateau Functions. ACM Trans. Evol. Learn. Optim. 1(4): 13:1-13:28 (2021) - [j99]Benjamin Doerr, Frank Neumann:
A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization. ACM Trans. Evol. Learn. Optim. 1(4): 16:1-16:43 (2021) - [c170]Benjamin Doerr, Weijie Zheng:
Theoretical Analyses of Multi-Objective Evolutionary Algorithms on Multi-Modal Objectives. AAAI 2021: 12293-12301 - [c169]Quentin Renau, Johann Dréo, Carola Doerr, Benjamin Doerr:
Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions. EvoApplications 2021: 17-33 - [c168]Riade Benbaki, Ziyad Benomar, Benjamin Doerr:
A rigorous runtime analysis of the 2-MMASib on jump functions: ant colony optimizers can cope well with local optima. GECCO 2021: 4-13 - [c167]Benjamin Doerr:
Runtime analysis via symmetry arguments: (hot-off-the-press track at GECCO 2021). GECCO Companion 2021: 23-24 - [c166]Benjamin Doerr, Weijie Zheng:
Theoretical analyses of multi-objective evolutionary algorithms on multi-modal objectives: (hot-off-the-press track at GECCO 2021). GECCO Companion 2021: 25-26 - [c165]Benjamin Doerr:
A gentle introduction to theory (for non-theoreticians). GECCO Companion 2021: 369-398 - [c164]Denis Antipov, Maxim Buzdalov, Benjamin Doerr:
Lazy parameter tuning and control: choosing all parameters randomly from a power-law distribution. GECCO 2021: 1115-1123 - [c163]Henry Bambury, Antoine Bultel, Benjamin Doerr:
Generalized jump functions. GECCO 2021: 1124-1132 - [c162]Benjamin Doerr, Timo Kötzing:
Lower bounds from fitness levels made easy. GECCO 2021: 1142-1150 - [c161]Shouda Wang, Weijie Zheng, Benjamin Doerr:
Choosing the Right Algorithm With Hints From Complexity Theory. IJCAI 2021: 1697-1703 - [d2]Quentin Renau, Johann Dréo, Carola Doerr, Benjamin Doerr:
Exploratory Landscape Analysis Feature Values for the 24 Noiseless BBOB Functions. Zenodo, 2021
- [i94]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) - [i93]