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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
- 2023
[j119]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)- [j118]Benjamin Doerr, Amirhossein Rajabi:
Stagnation detection meets fast mutation. Theor. Comput. Sci. 946: 113670 (2023) - [i118]Benjamin Doerr, Andrew James Kelley:
Fourier Analysis Meets Runtime Analysis: Precise Runtimes on Plateaus. CoRR abs/2302.08021 (2023) - [i117]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) - [i116]Firas Ben Jedidia, Benjamin Doerr, Martin S. Krejca:
Estimation-of-Distribution Algorithms for Multi-Valued Decision Variables. CoRR abs/2302.14420 (2023) - [i115]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) - [i114]Benjamin Doerr, Anatolii Kostrygin:
Randomized Rumor Spreading Revisited (Long Version). CoRR abs/2303.11150 (2023) - [i113]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) - [i112]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) - [i111]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) - [i110]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) - 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]Benjamin Doerr:
A Sharp Discrepancy Bound for Jittered Sampling. CoRR abs/2103.15712 (2021) - [i92]Benjamin Doerr, Timo Kötzing:
Lower Bounds from Fitness Levels Made Easy. CoRR abs/2104.03372 (2021) - [i91]Denis Antipov, Maxim Buzdalov, Benjamin Doerr:
Lazy Parameter Tuning and Control: Choosing All Parameters Randomly From a Power-Law Distribution. CoRR abs/2104.06714 (2021) - [i90]Benjamin Doerr, Michael Gnewuch:
On Negative Dependence Properties of Latin Hypercube Samples and Scrambled Nets. CoRR abs/2104.10799 (2021) - [i89]Henry Bambury, Antoine Bultel, Benjamin Doerr:
An Extended Jump Function Benchmark for the Analysis of Randomized Search Heuristics. CoRR abs/2105.03090 (2021) - [i88]Shouda Wang, Weijie Zheng, Benjamin Doerr:
Choosing the Right Algorithm With Hints From Complexity Theory. CoRR abs/2109.06584 (2021) - [i87]Weijie Zheng, Yufei Liu, Benjamin Doerr:
A First Mathematical Runtime Analysis of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). CoRR abs/2112.08581 (2021) - 2020
- [j98]Benjamin Doerr, Carola Doerr, Jing Yang:
Optimal parameter choices via precise black-box analysis. Theor. Comput. Sci. 801: 1-34 (2020) - [j97]Benjamin Doerr, Weijie Zheng:
Working principles of binary differential evolution. Theor. Comput. Sci. 801: 110-142 (2020) - [j96]Benjamin Doerr, Timo Kötzing, J. A. Gregor Lagodzinski, Johannes Lengler:
The impact of lexicographic parsimony pressure for ORDER/MAJORITY on the run time. Theor. Comput. Sci. 816: 144-168 (2020) - [j95]Benjamin Doerr, Martin S. Krejca:
Significance-Based Estimation-of-Distribution Algorithms. IEEE Trans. Evol. Comput. 24(6): 1025-1034 (2020) - [j94]Benjamin Doerr, Weijie Zheng:
Sharp Bounds for Genetic Drift in Estimation of Distribution Algorithms. IEEE Trans. Evol. Comput. 24(6): 1140-1149 (2020) - [j93]Benjamin Doerr, Marvin Künnemann:
Improved Protocols and Hardness Results for the Two-Player Cryptogenography Problem. IEEE Trans. Inf. Theory 66(9): 5729-5741 (2020) - [c160]Benjamin Doerr, Carola Doerr, Aneta Neumann, Frank Neumann, Andrew M. Sutton:
Optimization of Chance-Constrained Submodular Functions. AAAI 2020: 1460-1467 - [c159]Benjamin Doerr, Martin S. Krejca:
The Univariate Marginal Distribution Algorithm Copes Well with Deception and Epistasis. EvoCOP 2020: 51-66 - [c158]Benjamin Doerr, Weijie Zheng:
Sharp bounds for genetic drift in estimation of distribution algorithms (Hot-off-the-press track at GECCO 2020). GECCO Companion 2020: 15-16 - [c157]Benjamin Doerr, Martin S. Krejca:
The univariate marginal distribution algorithm copes well with deception and epistasis. GECCO Companion 2020: 17-18 - [c156]Benjamin Doerr:
A gentle introduction to theory (for non-theoreticians). GECCO Companion 2020: 373-403 - [c155]Benjamin Doerr, Martin S. Krejca:
Bivariate estimation-of-distribution algorithms can find an exponential number of optima. GECCO 2020: 796-804 - [c154]Benjamin Doerr, Weijie Zheng:
From understanding genetic drift to a smart-restart parameter-less compact genetic algorithm. GECCO 2020: 805-813 - [c153]Denis Antipov, Benjamin Doerr, Vitalii Karavaev:
The (1 + (λ, λ)) GA is even faster on multimodal problems. GECCO 2020: 1259-1267 - [c152]Denis Antipov, Maxim Buzdalov, Benjamin Doerr:
Fast mutation in crossover-based algorithms. GECCO 2020: 1268-1276 - [c151]Maxim Buzdalov, Benjamin Doerr, Carola Doerr, Dmitry Vinokurov:
Fixed-target runtime analysis. GECCO 2020: 1295-1303 - [c150]Benjamin Doerr:
Does comma selection help to cope with local optima? GECCO 2020: 1304-1313 - [c149]Quentin Renau, Carola Doerr, Johann Dréo, Benjamin Doerr:
Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy. PPSN (2) 2020: 139-153 - [c148]Denis Antipov, Benjamin Doerr:
Runtime Analysis of a Heavy-Tailed (1+(λ , λ )) Genetic Algorithm on Jump Functions. PPSN (2) 2020: 545-559 - [c147]Denis Antipov, Maxim Buzdalov, Benjamin Doerr:
First Steps Towards a Runtime Analysis When Starting with a Good Solution. PPSN (2) 2020: 560-573 - [c146]Benjamin Doerr:
Lower Bounds for Non-elitist Evolutionary Algorithms via Negative Multiplicative Drift. PPSN (2) 2020: 604-618 - [c145]Benjamin Doerr:
Exponential Upper Bounds for the Runtime of Randomized Search Heuristics. PPSN (2) 2020: 619-633 - [p4]Benjamin Doerr:
Probabilistic Tools for the Analysis of Randomized Optimization Heuristics. Theory of Evolutionary Computation 2020: 1-87 - [p3]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 - [e2]Benjamin Doerr, Frank Neumann:
Theory of Evolutionary Computation - Recent Developments in Discrete Optimization. Natural Computing Series, Springer 2020, ISBN 978-3-030-29413-7 [contents] - [d1]Quentin Renau, Carola Doerr, Johann Dréo, Benjamin Doerr:
Experimental Data Set for the study "Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy". Zenodo, 2020 - [i86]Benjamin Doerr:
Does Comma Selection Help To Cope With Local Optima. CoRR abs/2004.01274 (2020) - [i85]Benjamin Doerr, Martin S. Krejca:
A Simplified Run Time Analysis of the Univariate Marginal Distribution Algorithm on LeadingOnes. CoRR abs/2004.04978 (2020) - [i84]Benjamin Doerr:
Exponential Upper Bounds for the Runtime of Randomized Search Heuristics. CoRR abs/2004.05733 (2020) - [i83]Denis Antipov, Maxim Buzdalov, Benjamin Doerr:
Fast Mutation in Crossover-based Algorithms. CoRR abs/2004.06538 (2020) - [i82]Denis Antipov, Benjamin Doerr, Vitalii Karavaev:
The (1 + (λ, λ)) GA Is Even Faster on Multimodal Problems. CoRR abs/2004.06702 (2020) - [i81]Benjamin Doerr, Weijie Zheng:
From Understanding Genetic Drift to a Smart-Restart Parameter-less Compact Genetic Algorithm. CoRR abs/2004.07141 (2020) - [i80]Maxim Buzdalov, Benjamin Doerr, Carola Doerr, Dmitry Vinokurov:
Fixed-Target Runtime Analysis. CoRR abs/2004.09613 (2020) - [i79]Benjamin Doerr:
Lower Bounds for Non-Elitist Evolutionary Algorithms Via Negative Multiplicative Drift. CoRR abs/2005.00853 (2020) - [i78]Denis Antipov, Benjamin Doerr:
Runtime Analysis of a Heavy-Tailed (1+(λ, λ)) Genetic Algorithm on Jump Functions. CoRR abs/2006.03523 (2020) - [i77]Benjamin Doerr:
Runtime Analysis of Evolutionary Algorithms via Symmetry Arguments. CoRR abs/2006.04663 (2020) - [i76]Quentin Renau, Carola Doerr, Johann Dréo, Benjamin Doerr:
Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy. CoRR abs/2006.11135 (2020) - [i75]Denis Antipov, Maxim Buzdalov, Benjamin Doerr:
First Steps Towards a Runtime Analysis When Starting With a Good Solution. CoRR abs/2006.12161 (2020) - [i74]Benjamin Doerr, Frank Neumann:
A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization. CoRR abs/2006.16709 (2020) - [i73]Benjamin Doerr, Martin S. Krejca:
The Univariate Marginal Distribution Algorithm Copes Well With Deception and Epistasis. CoRR abs/2007.08277 (2020) - [i72]Benjamin Doerr, Weijie Zheng:
Theoretical Analyses of Multi-Objective Evolutionary Algorithms on Multi-Modal Objectives. CoRR abs/2012.07231 (2020)
2010 – 2019
- 2019
- [j92]Benjamin Doerr, Christian Gießen, Carsten Witt, Jing Yang:
The (1+λ) Evolutionary Algorithm with Self-Adjusting Mutation Rate. Algorithmica 81(2): 593-631 (2019) - [j91]Benjamin Doerr, Carola Doerr, Timo Kötzing:
Solving Problems with Unknown Solution Length at Almost No Extra Cost. Algorithmica 81(2): 703-748 (2019) - [j90]Benjamin Doerr, Philipp Fischbeck, Clemens Frahnow, Tobias Friedrich, Timo Kötzing, Martin Schirneck:
Island Models Meet Rumor Spreading. Algorithmica 81(2): 886-915 (2019) - [j89]