Evolving Asynchronous Cellular Automata for Density Classification
Francis Jeanson · 2008
This paper presents the comparative results of applying the same genetic algorithm (GA) for the evolution of both syn-chronous and randomly updated asynchronous cellular au-tomata (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 perform-ing asynchronous CA find equally good solutions on average to the density classification task in fewer computational steps than synchronous CA.