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Simply modified GKL density classifiers that reach consensus faster

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Autor(es):
Mendonca, J. Ricardo G.
Número total de Autores: 1
Tipo de documento: Artigo Científico
Fonte: Physics Letters A; v. 383, n. 19, p. 2264-2266, JUL 8 2019.
Citações Web of Science: 0
Resumo

The two-state Gacs-Kurdyumov-Levin (GKL) cellular automaton has been a staple model in the study of complex systems due to its ability to classify binary arrays of symbols according to their initial density. We show that a class of modified GKL models over extended neighborhoods, but still involving only three cells at a time, achieves comparable density classification performance but in some cases reach consensus more than twice as fast. Our results suggest the time to consensus (relative to the length of the CA) as a complementary measure of density classification performance. (C) 2019 Elsevier B.V. All rights reserved. (AU)

Processo FAPESP: 17/22166-9 - Recordes, alcance e maiores subsequências crescentes de passeios aleatórios
Beneficiário:José Ricardo Gonçalves de Mendonça
Modalidade de apoio: Bolsas no Exterior - Pesquisa