Learning in abstract memory schemes for dynamic optimization.

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dc.contributor.authorRichter, Hendriken
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2013-06-11T16:01:39Z
dc.date.available2013-06-11T16:01:39Z
dc.date.issued2008
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
dc.identifier.citationRichter, H. and Yang, S. (2008) Learning in abstract memory schemes for dynamic optimization. In: Proceedings of the 4th International Conference on Natural Computation, Jinan, China, October 2008. Vol. 1. New York: IEEE, pp. 86-91.en
dc.identifier.doihttps://doi.org/10.1109/ICNC.2008.110
dc.identifier.isbn978-0-7695-3304-9
dc.identifier.urihttp://hdl.handle.net/2086/8730
dc.language.isoenen
dc.peerreviewedYesen
dc.publisherIEEEen
dc.researchgroupCentre for Computational Intelligenceen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectAbstract memoryen
dc.subjectEvolutionary algorithmen
dc.subjectLearningen
dc.titleLearning in abstract memory schemes for dynamic optimization.en
dc.typeArticleen

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