A step size based self-adaptive mutation operator for Evolutionary Programming

Libin Hong, John Drake, Ender Özcan

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

The mutation operator is the only genetic operator in Evolutionary Programming (EP). In the past researchers have nominated Gaussian, Cauchy, and Lévy distributions as mutation operators. According to the No Free Lunch theorem [9], no single mutation operator is able to outperform all others over the set of all possible functions. Potentially there is a lot of useful information generated when EP is ongoing. In this paper, we collect such information and propose a step size based self-adaptive mutation operator for Evolutionary Programming (SSEP). In SSEP, the mutation operator might be changed during the evolutionary process, based on the step size, from generation to generation. Principles for selecting an appropriate mutation operator for EP is proposed, with SSEP grounded on the principles. SSEP is shown to outperform static mutation operators in Evolutionary Programming on most of the functions tested. We also compare the experimental results of SSEP with other recent Evolutionary Programming methods, which uses multiple mutation operators.

Original languageEnglish
Title of host publicationGECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery
Pages1381-1387
Number of pages7
ISBN (Print)9781450328814
DOIs
Publication statusPublished - 2014
Event16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion - Vancouver, BC, Canada
Duration: 12 Jul 201416 Jul 2014

Publication series

NameGECCO 2014 - Companion Publication of the 2014 Genetic and Evolutionary Computation Conference

Conference

Conference16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion
Country/TerritoryCanada
CityVancouver, BC
Period12/07/1416/07/14

Keywords

  • Convergence
  • Evolutionary Programming
  • Function optimization
  • Mutation operator
  • Self-adaptive
  • Step size

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Applied Mathematics

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