Evolving Asynchronous Cellular Automata for Density Classification
نویسنده
چکیده
This paper presents the comparative results of applying the same genetic algorithm (GA) for the evolution of both synchronous and randomly updated asynchronous cellular automata (CA) for the computationally emergent task of density classification. The present results indicate not only that these asynchronous CA evolve more quickly and consistently than their synchronous counterparts, but also that the best performing asynchronous CA find equally good solutions on average to the density classification task in fewer computational steps than synchronous CA.
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