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Evolving Cellular Automata

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Cellular Automata

Part of the book series: Encyclopedia of Complexity and Systems Science Series ((ECSSS))

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  • Originally published in
  • R. A. Meyers (ed.), Encyclopedia of Complexity and Systems Science, © Springer-Verlag 2009

Glossary

Cellular automaton (CA):

Discrete-space and discrete-time spatially extended lattice of cells connected in a regular pattern. Each cell stores its state and a state-transition function. At each time step, each cell applies the transition function to update its state based on its local neighborhood of cell states. The update of the system is performed in synchronous steps – i.e., all cells update simultaneously.

Cellular programming:

A variation of genetic algorithms designed to simultaneously evolve state transition rules and local neighborhood connection topologies for non-homogeneous cellular automata.

Coevolution:

An extension to the genetic algorithm in which candidate solutions and their “environment” (typically test cases) are evolved simultaneously.

Density classification:

A computational task for binary CAs: the desired behavior for the CA is to iterate to an all-1s configuration if the initial configuration has a majority of cells in state 1, and to an all-0s...

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Acknowledgments

This work has been funded by the Center on Functional Engineered Nano Architectonics (FENA), through the Focus Center Research Program of the Semiconductor Industry Association.

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Authors and Affiliations

  1. Computer Science Department, Portland State University, Portland, OR, USA

    Martin Cenek & Melanie Mitchell

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  1. Martin Cenek
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  2. Melanie Mitchell
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Editors and Affiliations

  1. Unconventional Computing Centre, University of the West of England, Bristol, UK

    Andrew Adamatzky

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Cenek, M., Mitchell, M. (2009). Evolving Cellular Automata. In: Adamatzky, A. (eds) Cellular Automata. Encyclopedia of Complexity and Systems Science Series. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-8700-9_191

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