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A genetic algorithm discovers particle-based computation in cellular automata

  • Rajarshi Das
  • Melanie Mitchell
  • James P. Crutchfield
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 866)

Abstract

How does evolution produce sophisticated emergent computation in systems composed of simple components limited to local interactions? To model such a process, we used a genetic algorithm (GA) to evolve cellular automata to perform a computational task requiring globally-coordinated information processing. On most runs a class of relatively unsophisticated strategies was evolved, but on a subset of runs a number of quite sophisticated strategies was discovered. We analyze the emergent logic underlying these strategies in terms of information processing performed by “particles” in space-time, and we describe in detail the generational progression of the GA evolution of these strategies. Our analysis is a preliminary step in understanding the general mechanisms by which sophisticated emergent computational capabilities can be automatically produced in decentralized multiprocessor systems.

Keywords

Genetic Algorithm Cellular Automaton Initial Configuration Regular Domain Deterministic Finite Automaton 
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 1994

Authors and Affiliations

  • Rajarshi Das
    • 1
  • Melanie Mitchell
    • 1
  • James P. Crutchfield
    • 2
  1. 1.Santa Fe InstituteSanta FeUSA
  2. 2.Physics DepartmentUniversity of CaliforniaBerkeleyUSA

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