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A restart CMA evolution strategy with increasing population size

  • ETH Zurich

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

918 Citations (Scopus)

Abstract

In this paper we introduce a restart-CMA-evolution strategy, where the population size is increased for each restart (IPOP). By increasing the population size the search characteristic becomes more global after each restart. The IPOP-CMA-ES is evaluated on the test suit of 25 functions designed for the special session on real-parameter optimization of CEC 2005. Its performance is compared to a local restart strategy with constant small population size. On unimodal functions the performance is similar. On multi-modal functions the local restart strategy significantly outperforms IPOP in 4 test cases whereas IPOP performs significantly better in 29 out of 60 tested cases.

Original languageEnglish
Title of host publication2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
PublisherIEEE Computer Society
Pages1769-1776
Number of pages8
ISBN (Print)0780393635, 9780780393639
DOIs
Publication statusPublished - 1 Jan 2005
Externally publishedYes
Event2005 IEEE Congress on Evolutionary Computation, CEC 2005 - Edinburgh, Scotland, United Kingdom
Duration: 2 Sept 20055 Sept 2005

Publication series

Name2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Volume2

Conference

Conference2005 IEEE Congress on Evolutionary Computation, CEC 2005
Country/TerritoryUnited Kingdom
CityEdinburgh, Scotland
Period2/09/055/09/05

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