Coevolutionary search for one-dimensional cellular automata, based on parameters related to their dynamic behaviour

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dc.contributor.authorOliveira G.M.B.
dc.contributor.authorAsakura O.K.N.
dc.contributor.authorDe Oliveira P.P.B.
dc.date.accessioned2024-03-13T01:45:43Z
dc.date.available2024-03-13T01:45:43Z
dc.date.issued2003
dc.description.abstractThe understanding of how cellular automata (CA) carry out arbitrary computations through totally local and parallel processing and how to harness their programmability is still extremely vague. In order to face this question various evolutionary methods have been used to look for cellular automata of a predefined computational behaviour. In this context, the most widely studied CA task is the density classification task (DCT), the best rule currently known for it having been obtained by a coevolutionary genetic algorithm (CGA). Extending our previous success in incorporating a parameter-based heuristic into a standard, single population genetic algorithm in the DCT, here we analyse the influence of incorporating that heuristic into the coevolutionary search. The results obtained show that the parameters can effectively help a CGA in searching for DCT rules, and suggest that the choice of the amount of bias in the search, allowed for the heuristic is more sensitive than in the standard genetic search.
dc.description.firstpage99
dc.description.issuenumber2-4
dc.description.lastpage110
dc.description.volume13
dc.identifier.issn1064-1246
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/37968
dc.relation.ispartofJournal of Intelligent and Fuzzy Systems
dc.rightsAcesso Restrito
dc.titleCoevolutionary search for one-dimensional cellular automata, based on parameters related to their dynamic behaviour
dc.typeArtigo
local.scopus.citations0
local.scopus.eid2-s2.0-0142059040
local.scopus.subjectDensity classification task (DCT)
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=0142059040&origin=inward
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