DAFHEA: A Dynamic Approximate Fitness based Hybrid Evolutionary Algorithm for optimisation problems

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Abstract

A dynamic approximate fitness-based hybrid evolutionary algorithm is presented here. The proposed model partially replaces expensive fitness evaluation by an approximate model. A cluster-based intelligent guided technique is used to decide on use of expensive function evaluation and dynamically adapt the predicted model. Avoiding expensive function evaluations speeds up the optimisation process. Also additional information derived from the predicted model at lower computational expense, is exploited to improve solution. Experimental findings support the theoretical basis of the proposed framework.
Original languageEnglish
Title of host publicationIEEE Congress on Evolutionary Computation 2003 (CEC 2003)
Place of PublicationUSA
PublisherIEEE Press
Pages1879-1886
Number of pages8
Volume3
ISBN (Electronic)0780378059
DOIs
Publication statusPublished - 2003
EventIEEE Congress on Evolutionary Computation - Canberra, Australia, Australia
Duration: 08 Dec 200312 Dec 2003

Conference

ConferenceIEEE Congress on Evolutionary Computation
Country/TerritoryAustralia
Period08/12/0312/12/03

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