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Better Runtime Guarantees Via Stochastic Domination
v1v2v3v4v5 (latest)

Better Runtime Guarantees Via Stochastic Domination

13 January 2018
Benjamin Doerr
ArXiv (abs)PDFHTML

Papers citing "Better Runtime Guarantees Via Stochastic Domination"

41 / 41 papers shown
Runtime Analyses of NSGA-III on Many-Objective Problems
Runtime Analyses of NSGA-III on Many-Objective Problems
Andre Opris
D. Dang
Frank Neumann
Dirk Sudholt
316
40
0
17 Apr 2024
A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical
  Analysis
A Block-Coordinate Descent EMO Algorithm: Theoretical and Empirical Analysis
Benjamin Doerr
Joshua Knowles
Aneta Neumann
Frank Neumann
264
1
0
04 Apr 2024
Larger Offspring Populations Help the $(1 + (λ, λ))$ Genetic
  Algorithm to Overcome the Noise
Larger Offspring Populations Help the (1+(λ,λ))(1 + (λ, λ))(1+(λ,λ)) Genetic Algorithm to Overcome the Noise
A. Ivanova
Denis Antipov
Benjamin Doerr
268
0
0
08 May 2023
Comma Selection Outperforms Plus Selection on OneMax with Randomly
  Planted Optima
Comma Selection Outperforms Plus Selection on OneMax with Randomly Planted OptimaAnnual Conference on Genetic and Evolutionary Computation (GECCO), 2023
J. Jorritsma
Johannes Lengler
Dirk Sudholt
220
15
0
19 Apr 2023
(1+1) Genetic Programming With Functionally Complete Instruction Sets
  Can Evolve Boolean Conjunctions and Disjunctions with Arbitrarily Small Error
(1+1) Genetic Programming With Functionally Complete Instruction Sets Can Evolve Boolean Conjunctions and Disjunctions with Arbitrarily Small ErrorArtificial Intelligence (AIJ), 2023
Benjamin Doerr
Andrei Lissovoi
P. S. Oliveto
169
1
0
13 Mar 2023
Tight Runtime Bounds for Static Unary Unbiased Evolutionary Algorithms
  on Linear Functions
Tight Runtime Bounds for Static Unary Unbiased Evolutionary Algorithms on Linear FunctionsAnnual Conference on Genetic and Evolutionary Computation (GECCO), 2023
Carola Doerr
D. Janett
Johannes Lengler
279
3
0
23 Feb 2023
Using Automated Algorithm Configuration for Parameter Control
Using Automated Algorithm Configuration for Parameter ControlFoundations of Genetic Algorithms (FOGA), 2023
D. Chen
M. Buzdalov
Carola Doerr
Nguyen Dang
342
9
0
23 Feb 2023
Fourier Analysis Meets Runtime Analysis: Precise Runtimes on Plateaus
Fourier Analysis Meets Runtime Analysis: Precise Runtimes on PlateausAnnual Conference on Genetic and Evolutionary Computation (GECCO), 2023
Benjamin Doerr
A. J. Kelley
355
7
0
16 Feb 2023
The $(1+(λ,λ))$ Global SEMO Algorithm
The (1+(λ,λ))(1+(λ,λ))(1+(λ,λ)) Global SEMO AlgorithmAnnual Conference on Genetic and Evolutionary Computation (GECCO), 2022
Benjamin Doerr
O. Hadri
A. Pinard
200
12
0
07 Oct 2022
Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm
  Configuration
Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm ConfigurationAnnual Conference on Genetic and Evolutionary Computation (GECCO), 2022
André Biedenkapp
Nguyen Dang
Martin S. Krejca
Katharina Eggensperger
Carola Doerr
255
13
0
07 Feb 2022
Mathematical Runtime Analysis for the Non-Dominated Sorting Genetic
  Algorithm II (NSGA-II)
Mathematical Runtime Analysis for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II)
Weijie Zheng
Benjamin Doerr
414
76
0
16 Dec 2021
Choosing the Right Algorithm With Hints From Complexity Theory
Choosing the Right Algorithm With Hints From Complexity Theory
Shouda Wang
Weijie Zheng
Benjamin Doerr
501
20
0
14 Sep 2021
An Extended Jump Functions Benchmark for the Analysis of Randomized
  Search Heuristics
An Extended Jump Functions Benchmark for the Analysis of Randomized Search Heuristics
Henry Bambury
Antoine Bultel
Benjamin Doerr
425
13
0
07 May 2021
The Univariate Marginal Distribution Algorithm Copes Well With Deception
  and Epistasis
The Univariate Marginal Distribution Algorithm Copes Well With Deception and EpistasisEvolutionary Computation (Evol. Comput.), 2020
Benjamin Doerr
Martin S. Krejca
237
34
0
16 Jul 2020
A Survey on Recent Progress in the Theory of Evolutionary Algorithms for
  Discrete Optimization
A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization
Benjamin Doerr
Frank Neumann
389
41
0
30 Jun 2020
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the
  Mutation Rate of an Evolutionary Algorithm
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm
Arina Buzdalova
Carola Doerr
A. Rodionova
231
3
0
19 Jun 2020
Benchmarking a $(μ+λ)$ Genetic Algorithm with Configurable
  Crossover Probability
Benchmarking a (μ+λ)(μ+λ)(μ+λ) Genetic Algorithm with Configurable Crossover ProbabilityParallel Problem Solving from Nature (PPSN), 2020
Furong Ye
Hao Wang
Carola Doerr
Thomas Bäck
137
15
0
10 Jun 2020
Lower Bounds for Non-Elitist Evolutionary Algorithms via Negative
  Multiplicative Drift
Lower Bounds for Non-Elitist Evolutionary Algorithms via Negative Multiplicative DriftEvolutionary Computation (Evol. Comput.), 2020
Benjamin Doerr
228
20
0
02 May 2020
MATE: A Model-based Algorithm Tuning Engine
MATE: A Model-based Algorithm Tuning Engine
Mohamed El Yafrani
M. Martins
Inkyung Sung
Markus Wagner
Carola Doerr
Peter Nielsen
178
4
0
27 Apr 2020
From Understanding Genetic Drift to a Smart-Restart Parameter-less
  Compact Genetic Algorithm
From Understanding Genetic Drift to a Smart-Restart Parameter-less Compact Genetic AlgorithmAnnual Conference on Genetic and Evolutionary Computation (GECCO), 2020
Benjamin Doerr
Weijie Zheng
288
22
0
15 Apr 2020
Fast Mutation in Crossover-based Algorithms
Fast Mutation in Crossover-based Algorithms
Denis Antipov
M. Buzdalov
Benjamin Doerr
323
37
0
14 Apr 2020
Exponential Upper Bounds for the Runtime of Randomized Search Heuristics
Exponential Upper Bounds for the Runtime of Randomized Search HeuristicsParallel Problem Solving from Nature (PPSN), 2020
Benjamin Doerr
421
11
0
13 Apr 2020
Does Comma Selection Help To Cope With Local Optima
Does Comma Selection Help To Cope With Local Optima
Benjamin Doerr
322
56
0
02 Apr 2020
Benchmarking Discrete Optimization Heuristics with IOHprofiler
Benchmarking Discrete Optimization Heuristics with IOHprofilerAnnual Conference on Genetic and Evolutionary Computation (GECCO), 2019
Carola Doerr
Furong Ye
Naama Horesh
Hao Wang
O. M. Shir
Thomas Bäck
290
86
0
19 Dec 2019
Sharp Bounds for Genetic Drift in Estimation of Distribution Algorithms
Sharp Bounds for Genetic Drift in Estimation of Distribution AlgorithmsIEEE Transactions on Evolutionary Computation (TEVC), 2019
Benjamin Doerr
Weijie Zheng
278
38
0
31 Oct 2019
Merging variables: one technique of search in pseudo-Boolean
  optimization
Merging variables: one technique of search in pseudo-Boolean optimization
A. Semenov
75
5
0
02 Aug 2019
An Exponential Lower Bound for the Runtime of the cGA on Jump Functions
An Exponential Lower Bound for the Runtime of the cGA on Jump Functions
Benjamin Doerr
223
58
0
17 Apr 2019
The Efficiency Threshold for the Offspring Population Size of the
  ($μ$, $λ$) EA
The Efficiency Threshold for the Offspring Population Size of the (μμμ, λλλ) EA
Denis Antipov
Benjamin Doerr
Quentin Yang
197
23
0
15 Apr 2019
Multiplicative Up-Drift
Multiplicative Up-Drift
Benjamin Doerr
Timo Kotzing
320
46
0
11 Apr 2019
A Tight Runtime Analysis for the cGA on Jump Functions---EDAs Can Cross
  Fitness Valleys at No Extra Cost
A Tight Runtime Analysis for the cGA on Jump Functions---EDAs Can Cross Fitness Valleys at No Extra Cost
Benjamin Doerr
149
36
0
26 Mar 2019
Self-Adjusting Mutation Rates with Provably Optimal Success Rules
Self-Adjusting Mutation Rates with Provably Optimal Success Rules
Benjamin Doerr
Carola Doerr
Johannes Lengler
464
63
0
07 Feb 2019
Fast Re-Optimization via Structural Diversity
Fast Re-Optimization via Structural DiversityAnnual Conference on Genetic and Evolutionary Computation (GECCO), 2019
Benjamin Doerr
Carola Doerr
Frank Neumann
147
16
0
01 Feb 2019
Interpolating Local and Global Search by Controlling the Variance of
  Standard Bit Mutation
Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation
Furong Ye
Carola Doerr
Thomas Bäck
172
25
0
17 Jan 2019
A Tight Runtime Analysis for the $(μ+ λ)$ EA
A Tight Runtime Analysis for the (μ+λ)(μ+ λ)(μ+λ) EA
Denis Antipov
Benjamin Doerr
170
29
0
28 Dec 2018
Working Principles of Binary Differential Evolution
Working Principles of Binary Differential Evolution
Benjamin Doerr
Weijie Zheng
138
32
0
09 Dec 2018
Runtime Analysis for Self-adaptive Mutation Rates
Runtime Analysis for Self-adaptive Mutation Rates
Benjamin Doerr
Carsten Witt
Jing Yang
135
60
0
30 Nov 2018
Significance-based Estimation-of-Distribution Algorithms
Significance-based Estimation-of-Distribution Algorithms
Benjamin Doerr
Martin S. Krejca
257
52
0
10 Jul 2018
Optimal Parameter Choices via Precise Black-Box Analysis
Optimal Parameter Choices via Precise Black-Box Analysis
Benjamin Doerr
Carola Doerr
Jing Yang
240
109
0
09 Jul 2018
Simple Hyper-heuristics Control the Neighbourhood Size of Randomised
  Local Search Optimally for LeadingOnes
Simple Hyper-heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes
Andrei Lissovoi
P. S. Oliveto
J. A. Warwicker
299
2
0
23 Jan 2018
Probabilistic Tools for the Analysis of Randomized Optimization
  Heuristics
Probabilistic Tools for the Analysis of Randomized Optimization Heuristics
Benjamin Doerr
448
194
0
20 Jan 2018
Solving Problems with Unknown Solution Length at (Almost) No Extra Cost
Solving Problems with Unknown Solution Length at (Almost) No Extra Cost
Benjamin Doerr
Carola Doerr
Timo Kotzing
144
26
0
19 Jun 2015
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