Experimental study on population-based incremental learning algorithms for dynamic optimization problems

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dc.cclicenceN/Aen
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorYao, Xinen
dc.date.accessioned2017-03-07T11:20:31Z
dc.date.available2017-03-07T11:20:31Z
dc.date.issued2005-11-01
dc.description.abstractEvolutionary algorithms have been widely used for stationary optimization problems. However, the environments of real world problems are often dynamic. This seriously challenges traditional evolutionary algorithms. In this paper, the application of population-based incremental learning (PBIL) algorithms, a class of evolutionary algorithms, for dynamic problems is investigated. Inspired by the complementarity mechanism in nature a Dual PBIL is proposed, which operates on two probability vectors that are dual to each other with respect to the central point in the genotype space. A diversity maintaining technique of combining the central probability vector into PBIL is also proposed to improve PBIL’s adaptability in dynamic environments. In this paper, a new dynamic problem generator that can create required dynamics from any binary-encoded stationary problem is also formalized. Using this generator, a series of dynamic problems were systematically constructed from several benchmark stationary problems and an experimental study was carried out to compare the performance of several PBIL algorithms and two variants of standard genetic algorithm. Based on the experimental results, we carried out algorithm performance analysis regarding the weakness and strength of studied PBIL algorithms and identified several potential improvements to PBIL for dynamic optimization problems.en
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.identifier.citationYang, S. and Yao, X. (2005) Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Computing, 9 (11), pp. 815-834en
dc.identifier.doihttps://doi.org/10.1007/s00500-004-0422-3
dc.identifier.urihttp://hdl.handle.net/2086/13442
dc.language.isoen_USen
dc.peerreviewedYesen
dc.projectidGR/S79718/01en
dc.publisherSpringeren
dc.researchgroupCentre for Computational Intelligenceen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectPopulation-based incremental learningen
dc.subjectDynamic optimization problemen
dc.subjectDual population-based incremental learningen
dc.subjectGenetic algorithmen
dc.subjectCentral probability vectoren
dc.subjectExclusive-or operatoren
dc.titleExperimental study on population-based incremental learning algorithms for dynamic optimization problemsen
dc.typeArticleen

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