Local learning and search in memetic algorithms

Frederico G. Guimarães*, Elizabeth F. Wanner, Felipe Campelo, Ricardo H.C. Takahashi, Hajime Igarashi, David A. Lowther, Jaime A. Ramírez

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

The use of local search in evolutionary techniques is believed to enhance the performance of the algorithms, giving rise to memetic or hybrid algorithms. However, in many continuous optimization problems the additional cost required by local search may be prohibitive. Thus we propose the local learning of the objective and constraint functions prior to the local search phase of memetic algorithms, based on the samples gathered by the population through the evolutionary process. The local search operator is then applied over this approximated model. We perform some experiments by combining our approach with a real-coded genetic algorithm. The results demonstrate the benefit of the proposed methodology for costly black-box functions.

Original languageEnglish
Title of host publication2006 IEEE Congress on Evolutionary Computation, CEC 2006
PublisherIEEE
Pages2936-2943
Number of pages8
ISBN (Print)0780394879, 9780780394872
DOIs
Publication statusPublished - 1 Dec 2006
Event2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada
Duration: 16 Jul 200621 Jul 2006

Conference

Conference2006 IEEE Congress on Evolutionary Computation, CEC 2006
CountryCanada
CityVancouver, BC
Period16/07/0621/07/06

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