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Adaptive strategies applied to evolutionary search for 2D DCT cellular automata rules

Published:12 July 2011Publication History

ABSTRACT

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.

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      cover image ACM Conferences
      GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation
      July 2011
      2140 pages
      ISBN:9781450305570
      DOI:10.1145/2001576

      Copyright © 2011 ACM

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      • Published: 12 July 2011

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