Further results on multiobjective evolutionary search for one-dimensional, density classifier, cellular automata, and strategy analysis of the rules

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dc.contributor.authorOliveira G.M.B.
dc.contributor.authorBortot J.C.
dc.contributor.authorDe Oliveira P.P.B.
dc.date.accessioned2024-03-13T01:35:53Z
dc.date.available2024-03-13T01:35:53Z
dc.date.issued2009
dc.description.abstractA strong motivation for studying cellular automata (CA) is their ability to perform computations. However, the understanding of how these computations are carried out is still extremely vague, which makes the inverse problem of automatically designing CA rules with a predefined computational ability a fledgeling engineering endeavour. Various studies have been undertaken on methods to make CA design possible, one of them being the use of evolutionary computational techniques for searching the space of possible CA rules. A widely studied CA task is the Density Classification Task (DCT). For this and other tasks, it has recently been shown that the use of a heuristic guided by parameters that estimate the dynamic behaviour of CA can improve a standard evolutionary design. Considering the successful application of evolutionary multiobjective optimisation to several kinds of inverse problems, here one such technique known as Non-Dominated Sorting Genetic Algorithm is combined with the parameter-based heuristic, in the design of DCT rules. This is carried out in various alternative ways, yielding evolutionary searches with various numbers of objectives, of distinct qualities. With this exploration, it is shown that the resulting design scheme can effectively improve the search efficacy and obtain rules that solve the DCT with sophisticated strategies.
dc.description.firstpage133
dc.description.issuenumber1
dc.description.lastpage159
dc.description.volume186
dc.identifier.doi10.3233/978-1-58603-936-3-133
dc.identifier.issn1879-8314
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/37428
dc.relation.ispartofFrontiers in Artificial Intelligence and Applications
dc.rightsAcesso Restrito
dc.subject.otherlanguageCellular automata
dc.subject.otherlanguageDensity classification task
dc.subject.otherlanguageEvolutionary multiobjective optimization
dc.subject.otherlanguageInverse design
dc.subject.otherlanguageNon-dominated sorting genetic algorithm (NSGA)
dc.subject.otherlanguageParameter-based forecast of dynamic behaviour
dc.titleFurther results on multiobjective evolutionary search for one-dimensional, density classifier, cellular automata, and strategy analysis of the rules
dc.typeArtigo de evento
local.scopus.citations2
local.scopus.eid2-s2.0-72749119043
local.scopus.subjectAutomata rules
local.scopus.subjectCellular automatons
local.scopus.subjectDensity classification task
local.scopus.subjectDynamic behaviors
local.scopus.subjectEvolutionary multiobjective optimization
local.scopus.subjectEvolutionary search
local.scopus.subjectInverse designs
local.scopus.subjectNon-dominated sorting genetic algorithm
local.scopus.subjectNon-dominated sorting genetic algorithms
local.scopus.subjectParameter-based forecast of dynamic behavior
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=72749119043&origin=inward
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