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dc.contributor.authorRichter, H-
dc.contributor.authorYang, S-
dc.identifier.citationFourth International Conference on Natural Computation, Jinan, China, 25-27 August 2008, pp 86-91en_US
dc.description.abstractWe investigate an abstraction based memory scheme for evolutionary algorithms in dynamic environments. In this scheme, the abstraction of good solutions (i.e., their approximate location in the search space) is stored in the memory instead of good solutions themselves and is employed to improve future problem solving. In particular, this paper shows how learning takes place in the abstract memory scheme and how the performance in problem solving changes over time for different kinds of dynamics in the fitness landscape. The experiments show that the abstract memory enables learning processes and efficiently improves the performance of evolutionary algorithms in dynamic environments.en_US
dc.subjectAbstraction based memory schemeen_US
dc.subjectEvolutionary algorithmsen_US
dc.subjectDynamic environmentsen_US
dc.subjectDynamic optimization problems (DOPs)en_US
dc.titleLearning in abstract memory schemes for dynamic optimizationen_US
dc.typeConference Paperen_US
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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