How hard should we run? Trading off exploration and exploitation in evolutionary algorithms for dynamic optimisation problems

Yun Geun Lee*, Bob McKay

*Corresponding author for this work

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

Abstract

All evolutionary algorithms trade off exploration and exploitation in optimisation problems; dynamic problems are no exception. We investigate this trade-off, over a range of algorithm settings, on dynamic variants of three well-known optimisation problems (One Max, Royal Road and knapsack), using Yang's XOR method to vary the scale and rate of change. Extremely exploitative algorithm settings performed best for a surprisingly wide range of problems; even where they were not the most effective, they still performed competitively, and even in those cases, the best performers were still far more exploitative than most would anticipate.

Original languageEnglish
Title of host publicationGenetic and Evolutionary Computation Conference, GECCO'11
Pages1187-1194
Number of pages8
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 - Dublin, Ireland
Duration: 12 Jul 201116 Jul 2011

Publication series

NameGenetic and Evolutionary Computation Conference, GECCO'11

Conference

Conference13th Annual Genetic and Evolutionary Computation Conference, GECCO'11
Country/TerritoryIreland
CityDublin
Period12/07/1116/07/11

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