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Rearrangement Mechanism for Genetic Cellular Automata

Localization of interactions in Genetic Algorithms, in addition to speeding up the execution, improves the results significantly. One of the models for achieving this goal is Genetic Cellular Automata (GCA). In this paper we propose a mechanism called rearrangement mechanism to enhance the performance of GCA. We show that rearrangement of population in GCA could prevent populations from premature convergence in local neighborhoods and gives dissimilar individuals, from different localities, the opportunity to meet each other for reproduction. To show the effectiveness of the proposed rearrangement mechanism it has been tested on several problems including Knapsack, Traveling Sales Person, and Function Optimization problems.