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dc.contributor.authorRichter, H-
dc.contributor.authorYang, S-
dc.identifier.citationEvoWorkshops 2008: Applications of Evolutionary Computing, Lecture Notes in Computer Science 4974: 597 - 606, 2008en_US
dc.descriptionCopyright @ Springer-Verlag Berlin Heidelberg 2008.en_US
dc.description.abstractThis paper proposes a memory scheme based on abstraction for evolutionary algorithms to address dynamic optimization problems. In this memory scheme, the memory does not store good solutions as themselves but as their abstraction, i.e., their approximate location in the search space. When the environment changes, the stored abstraction information is extracted to generate new individuals into the population. Experiments are carried out to validate the abstraction based memory scheme. The results show the efficiency of the abstraction based memory scheme for evolutionary algorithms in dynamic environments.en_US
dc.description.sponsorshipThis work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant No. EP/E060722/1.en_US
dc.subjectEvolutionary Algorithmsen_US
dc.subjectDynamic optimization problemsen_US
dc.titleMemory based on abstraction for dynamic fitness functionsen_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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