Evolving Sequential Combinations of Elementary Cellular Automata Rules

  • Claudio L. M. Martins
  • Pedro P. B. de Oliveira
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3630)


Performing computations with cellular automata, individually or arranged in space or time, opens up new conceptual issues in emergent, artificial life type forms of computation, and opens up the possibility of novel technological advances. Here, a methodology for combining sequences of elementary cellular automata is presented, in order to perform a given computation. The problem at study is the well-known density classification task that consists of determining the most frequent bit in a binary string. The methodology relies on an evolutionary algorithm, together with analyses driven by background knowledge on dynamical behaviour of the rules and their parametric estimates, as well as those associated with the formal behaviour characterisation of the rules involved. The resulting methodology builds upon a previous approach available in the literature, and shows its efficacy by leading to 2 rule combinations already known, and to additional 26, apparently unknown so far.


Cellular Automaton Regular Expression Sequential Combination Density Classification Entire Lattice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Claudio L. M. Martins
    • 1
  • Pedro P. B. de Oliveira
    • 1
  1. 1.Universidade Presbiteriana MackenzieSão PauloBrazil

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