@inproceedings{4ac4e4a6e605443a912111f02e67ca26,

title = "Evolutionary algorithms with self-adjusting asymmetric mutation",

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 $$a\in \{0,1\}^n$$.",

keywords = "Asymmetric mutations, Evolutionary algorithms, Parameter control, Runtime analysis, Self-adjusting algorithms",

author = "Amirhossein Rajabi and Carsten Witt",

year = "2020",

doi = "10.1007/978-3-030-58112-1_46",

language = "English",

isbn = "9783030581114",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "664--677",

editor = "Thomas B{\"a}ck and Mike Preuss and Andr{\'e} Deutz and Michael Emmerich and Hao Wang and Carola Doerr and Heike Trautmann",

booktitle = "Parallel Problem Solving from Nature",

note = "16th International Conference on Parallel Problem Solving from Nature, PPSN 2020 ; Conference date: 05-09-2020 Through 09-09-2020",

}