Parallel Computing - Special issue on cellular automata: from modeling to applications
A new kind of science
Two-dimensional cellular automata of radius one for density classification task ρ = ½
Pattern Recognition Letters
IEEE Transactions on Parallel and Distributed Systems
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Electronic Notes in Theoretical Computer Science (ENTCS)
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Cellular automata (CA) are able to perform complex computations through local interactions. The investigation of how CA computations are carried out can be made by the usage of CA rules to solve specific tasks. The well-known problem called density classification task (DCT) is investigated, with focus on its two-dimensional version. Evolutionary algorithms have been widely used in the search for DCT rules. A sample of lattices with Gaussian distribution is commonly used to evaluate rule quality. However, uniform lattices are easier to classify, allowing an initial selective pressure needed to start the convergence. A comparative evaluation of three adaptive strategies is presented here: they start using easy lattices to classify and as effective rules are being obtained the difficult level is progressively increased toward the target evaluation. Several experiments were performed to evaluate the strategies efficiency and new rules were found, which outperform the best ones published.