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dc.contributor.authorConnor, AM
dc.contributor.editorKasabov, N
dc.contributor.editorChan, ZSH
dc.date.accessioned2014-04-10T07:51:51Z
dc.date.available2014-04-10T07:51:51Z
dc.date.copyright2006
dc.date.issued2014-04-10
dc.identifier.citationProceedings of the 6th International Conference on Hybrid Intelligent Systems (HIS’06) and the 6th International Conference on Neurocomputing and Evolving Intelligence (NCEI’06), Auckland, New Zealand, 2006-12-13 to 2006-12-15
dc.identifier.urihttp://hdl.handle.net/10292/7083
dc.description.abstractThis paper proposes that current memory models in use for tabu search algorithms are at best evolving, as opposed to adaptive, and that improvements can be made by considering the nature of human memory. By introducing new memory structures, the search method can learn about the solution space in which it is operating. The memory model is based on the transfer of events from episodic memory into generalised rules stored in semantic memory. By adopting this model, the algorithm can intelligently explore the solution space in response to what has been learned to date and continuously update the stored knowledge.
dc.publisherIEEE
dc.rightsCopyright © 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.titleMemory models for improving tabu search with real continuous variables
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.1109/HIS.2006.45
aut.conference.typePaper Published in Proceedings
pubs.elements-id42924


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