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dc.contributor.authorLikas, Aen
dc.contributor.authorBlekas, K.en
dc.contributor.authorStafylopatis, A.en
dc.date.accessioned2015-11-24T17:02:49Z-
dc.date.available2015-11-24T17:02:49Z-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/11097-
dc.rightsDefault Licence-
dc.titleParallel Recombinative Reinforcement Learning a genetic approachen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.publicationDate1996-
heal.abstractA 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.journalNameJournal of Intelligent Systemsen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
Appears in Collections:Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά)

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