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Fitness uniform deletion: a simple way to preserve diversity

Legg, Shane; Hutter, Marcus

Description

A commonly experienced problem with population based optimisation methods is the gradual decline in population diversity that tends to occur over time. This can slow a system's progress or even halt it completely if the population converges on a local optimum from which it cannot escape. In this paper we present the Fitness Uniform Deletion Scheme (FUDS), a simple but somewhat unconventional approach to this problem. Under FUDS the deletion operation is modified to only delete those individuals...[Show more]

dc.contributor.authorLegg, Shane
dc.contributor.authorHutter, Marcus
dc.date.accessioned2015-09-01T04:36:44Z
dc.date.available2015-09-01T04:36:44Z
dc.identifier.isbn1595930108
dc.identifier.urihttp://hdl.handle.net/1885/15048
dc.description.abstractA commonly experienced problem with population based optimisation methods is the gradual decline in population diversity that tends to occur over time. This can slow a system's progress or even halt it completely if the population converges on a local optimum from which it cannot escape. In this paper we present the Fitness Uniform Deletion Scheme (FUDS), a simple but somewhat unconventional approach to this problem. Under FUDS the deletion operation is modified to only delete those individuals which are "common" in the sense that there exist many other individuals of similar fitness in the population. This makes it impossible for the population to collapse to a collection of highly related individuals with similar fitness. Our experimental results on a range of optimisation problems confirm this, in particular for deceptive optimisation problems the performance is significantly more robust to variation in the selection intensity.
dc.publisherAssociation for Computing Mahcinery
dc.relation.ispartofGECCO 2005 : Genetic and Evolutionary Computation Conference, June 25-29, 2005 (Saturday-Wednesday) Washington, D.C., USA
dc.rights© 2005 ACM.
dc.subjectEvolutionary algorithm
dc.subjectdeletion schemes
dc.subjectfitness evaluation
dc.subjectoptimization
dc.subjectfitness landscapes
dc.subject(self)adaptation
dc.titleFitness uniform deletion: a simple way to preserve diversity
dc.typeConference paper
dc.date.issued2005
local.type.statusPublished Version
local.contributor.affiliationHutter, M., Research School of Computer Science, The Australian National University
local.bibliographicCitation.startpage1271
local.bibliographicCitation.lastpage1278
local.identifier.doi10.1145/1068009.1068216
CollectionsANU Research Publications

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