Runtime Analysis for Self-adaptive Mutation Rates

We propose and analyze a self-adaptive version of the ( 1 , λ ) evolutionary algorithm in which the current mutation rate is encoded within the individual and thus also subject to mutation. A rigorous runtime analysis on the OneMax benchmark function reveals that a simple local mutation scheme for t...

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Bibliographic Details
Published in:	Algorithmica Vol. 83; no. 4; pp. 1012 - 1053
Main Authors:	Doerr, Benjamin, Witt, Carsten, Yang, Jing
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.04.2021 Springer Nature B.V
Subjects:	2018 Algorithm Analysis and Problem Complexity Algorithms Asymptotic properties Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Drift Evolutionary algorithms Evolutionary computation Mathematics of Computing Mutation Optimization Parameters Run time (computers) Special Issue on Genetic and Evolutionary Computation Theory of Computation Two dimensional analysis Runtime analysis Self-adaptive Evolutionary algorithms
ISSN:	0178-4617, 1432-0541
Online Access:	Get full text
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