Evolutionary algorithms with self-adjusting asymmetric mutation

Amirhossein Rajabi*, Carsten Witt

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

Evolutionary Algorithms (EAs) and other randomized search heuristics are often considered as unbiased algorithms that are invariant with respect to different transformations of the underlying search space. However, if a certain amount of domain knowledge is available the use of biased search operators in EAs becomes viable. We consider a simple (1+1) EA for binary search spaces and analyze an asymmetric mutation operator that can treat zero- and one-bits differently. This operator extends previous work by Jansen and Sudholt (ECJ 18(1), 2010) by allowing the operator asymmetry to vary according to the success rate of the algorithm. Using a self-adjusting scheme that learns an appropriate degree of asymmetry, we show improved runtime results on the class of functions OneMax$$:a$$ describing the number of matching bits with a fixed target $$a\in \{0,1\}^n$$.

Original languageEnglish
Title of host publicationParallel Problem Solving from Nature
EditorsThomas Bäck, Mike Preuss, André Deutz, Michael Emmerich, Hao Wang, Carola Doerr, Heike Trautmann
PublisherSpringer
Publication date2020
Pages664-677
ISBN (Print)9783030581114
DOIs
Publication statusPublished - 2020
Event16th International Conference on Parallel Problem Solving from Nature, PPSN 2020 - Leiden, Netherlands
Duration: 5 Sep 20209 Sep 2020

Conference

Conference16th International Conference on Parallel Problem Solving from Nature, PPSN 2020
Country/TerritoryNetherlands
CityLeiden
Period05/09/202009/09/2020
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12269 LNCS
ISSN0302-9743

Keywords

  • Asymmetric mutations
  • Evolutionary algorithms
  • Parameter control
  • Runtime analysis
  • Self-adjusting algorithms

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