Parallel recombinative reinforcement learning: A genetic approach

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dc.contributor.author Likas, A en
dc.contributor.author Blekas, K en
dc.contributor.author Stafylopatis, A en
dc.date.accessioned 2014-03-01T01:44:49Z
dc.date.available 2014-03-01T01:44:49Z
dc.date.issued 1996 en
dc.identifier.issn 03341860 en
dc.identifier.uri http://hdl.handle.net/123456789/24494
dc.relation.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-0029698326&partnerID=40&md5=3d1ff6f0bdacbf3a8dde56486a5e41c0 en
dc.subject Genetic algorithms en
dc.subject Graph partitioning en
dc.subject Hybrid technique en
dc.subject Optimization en
dc.subject Parallel implementation en
dc.subject Reinforcement learning en
dc.subject.other Artificial intelligence en
dc.subject.other Genetic algorithms en
dc.subject.other Graph theory en
dc.subject.other Learning systems en
dc.subject.other Probability en
dc.subject.other Random processes en
dc.subject.other Hybrid technique en
dc.subject.other Parallel implementation en
dc.subject.other Optimization en
dc.title Parallel recombinative reinforcement learning: A genetic approach en
heal.type journalArticle en
heal.publicationDate 1996 en
heal.abstract A technique is presented that is suitable for function optimization in high-dimensional binary domains. The method allows an efficient parallel implementation and is based on the combination of genetic algorithms and reinforcement learning schemes. More specifically, a population of probability vectors is considered, each member corresponding to a reinforcement learning optimizer. Each probability vector represents the adaptable parameters of a team of stochastic units whose binary outputs provide a point of the function state space. At each step of the proposed technique the population members are updated according to a reinforcement learning rule and then recombined in a manner analogous to traditional genetic algorithm operation. Special care is devoted to ensuring the desirable properties of sustained exploration capability and sustained population diversity. The method has been tested on the graph partitioning problem in comparison with other techniques under two different types of fitness evaluation yielding very promising results. en
heal.journalName Journal of Intelligent Systems en
dc.identifier.volume 6 en
dc.identifier.issue 2 en
dc.identifier.spage 145 en
dc.identifier.epage 169 en

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