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Offspring Population Size Matters when Comparing Evolutionary Algorithms
  with Self-Adjusting Mutation Rates
v1v2 (latest)

Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates

17 April 2019
A. Rodionova
Kirill Antonov
Arina Buzdalova
Carola Doerr
ArXiv (abs)PDFHTML

Papers citing "Offspring Population Size Matters when Comparing Evolutionary Algorithms with Self-Adjusting Mutation Rates"

6 / 6 papers shown
Self-adjusting Population Sizes for the $(1, λ)$-EA on Monotone
  Functions
Self-adjusting Population Sizes for the (1,λ)(1, λ)(1,λ)-EA on Monotone FunctionsParallel Problem Solving from Nature (PPSN), 2022
Marc Kaufmann
Maxime Larcher
Johannes Lengler
Xun Zou
326
11
0
01 Apr 2022
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
198
3
0
19 Jun 2020
Evolutionary Algorithms with Self-adjusting Asymmetric Mutation
Evolutionary Algorithms with Self-adjusting Asymmetric Mutation
A. Rajabi
Carsten Witt
281
8
0
16 Jun 2020
Self-Adjusting Evolutionary Algorithms for Multimodal Optimization
Self-Adjusting Evolutionary Algorithms for Multimodal Optimization
A. Rajabi
Carsten Witt
367
68
0
07 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
278
82
0
19 Dec 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
433
61
0
07 Feb 2019
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