# Evolutionary algorithms with self-adjusting asymmetric mutation

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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 language English Parallel Problem Solving from Nature Thomas Bäck, Mike Preuss, André Deutz, Michael Emmerich, Hao Wang, Carola Doerr, Heike Trautmann Springer 2020 664-677 9783030581114 https://doi.org/10.1007/978-3-030-58112-1_46 Published - 2020 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020 - Leiden, NetherlandsDuration: 5 Sep 2020 → 9 Sep 2020

### Conference

Conference 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020 Netherlands Leiden 05/09/2020 → 09/09/2020
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12269 LNCS 0302-9743

## Keywords

• Asymmetric mutations
• Evolutionary algorithms
• Parameter control
• Runtime analysis