Evolutionary algorithms with self-adjusting asymmetric mutation

Amirhossein Rajabi*, Carsten Witt

*Corresponding author for this work

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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
Publication date2020
ISBN (Print)9783030581114
Publication statusPublished - 2020
Event16th International Conference on Parallel Problem Solving from Nature, PPSN 2020 - Leiden, Netherlands
Duration: 5 Sep 20209 Sep 2020


Conference16th International Conference on Parallel Problem Solving from Nature, PPSN 2020
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12269 LNCS


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


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