Runtime Analysis for Self-adaptive Mutation Rates

• 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

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...