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The Evolutionary Design of Collective Computation in Cellular Automata

Author

Listed:
  • James P. Crutchfield
  • Melanie Mitchell
  • Rajarshi Das

Abstract

We investigate the ability of a genetic algorithm to design cellular automata that perform computations. The computational strategies of the resulting cellular automata can be understood using a framework in which "particles" embedded in space-time configurations carry information and interactions between particles effect information processing. This structural analysis can also be used to explain the evolutionary process by which the strategies were designed by the genetic algorithm. More generally, our goals are to understand how machine-learning processes can design complex decentralized systems with sophisticated collective computational abilities and to develop rigorous frameworks for understanding how the resulting dynamical systems perform computation. Subitted to Machine Learning Journal.

Suggested Citation

  • James P. Crutchfield & Melanie Mitchell & Rajarshi Das, 1998. "The Evolutionary Design of Collective Computation in Cellular Automata," Working Papers 98-09-080, Santa Fe Institute.
    • Handle: RePEc:wop:safiwp:98-09-080
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    Cited by:

    1. Melanie Mitchell, 2000. "Life and Evolution in Computers," Working Papers 00-01-001, Santa Fe Institute.
    2. Justin Werfel & Melanie Mitchell & James P. Crutchfield, 1999. "Resource Sharing and Coevolution in Evolving Cellular Automata," Working Papers 99-07-045, Santa Fe Institute.

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