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dc.contributor.authorYang, S-
dc.identifier.citationEvoWorkshops 2007: Applications of Evolutionary Computing, Lecture Notes in Computer Science 4448: 627 - 636, 2007en_US
dc.descriptionCopyright @ Springer-Verlag Berlin Heidelberg 2007.en_US
dc.description.abstractAddressing dynamic optimization problems has been a challenging task for the genetic algorithm community. Over the years, several approaches have been developed into genetic algorithms to enhance their performance in dynamic environments. One major approach is to maintain the diversity of the population, e.g., via random immigrants. This paper proposes an elitism-based immigrants scheme for genetic algorithms in dynamic environments. In the scheme, the elite from previous generation is used as the base to create immigrants via mutation to replace the worst individuals in the current population. This way, the introduced immigrants are more adapted to the changing environment. This paper also proposes a hybrid scheme that combines the elitism-based immigrants scheme with traditional random immigrants scheme to deal with significant changes. The experimental results show that the proposed elitism-based and hybrid immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.en_US
dc.subjectDynamic optimization problemsen_US
dc.subjectGenetic algorithmsen_US
dc.subjectRandom immigrantsen_US
dc.subjectElitism-based immigrantsen_US
dc.titleGenetic algorithms with elitism-based immigrants for changing optimization problemsen_US
dc.typeBook Chapteren_US
pubs.organisational-data/Brunel/Brunel (Active)-
pubs.organisational-data/Brunel/Brunel (Active)/School of Info. Systems, Comp & Maths-
pubs.organisational-data/Brunel/Research Centres (RG)-
pubs.organisational-data/Brunel/Research Centres (RG)/CIKM-
pubs.organisational-data/Brunel/School of Information Systems, Computing and Mathematics (RG)-
pubs.organisational-data/Brunel/School of Information Systems, Computing and Mathematics (RG)/CIKM-
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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