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A symbiosis between cellular automata and genetic algorithms

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  • Cerruti, Umberto
  • Dutto, Simone
  • Murru, Nadir

Abstract

Cellular automata are systems which use a rule to describe the evolution of a population in a discrete lattice, while genetic algorithms are procedures designed to find solutions to optimization problems inspired by the process of natural selection. In this paper, we introduce an original implementation of a cellular automaton whose rules use a fitness function to select for each cell the best mate to reproduce and a crossover operator to determine the resulting offspring. This new system, with a proper definition, can be both a cellular automaton and a genetic algorithm. We show that in our system the Conway’s Game of Life can be easily implemented and, consequently, it is capable of universal computing. Moreover two generalizations of the Game of Life are created and also implemented with it. Finally, we use our system for studying and implementing the prisoner’s dilemma and rock-paper-scissors games, showing very interesting behaviors and configurations (e.g., gliders) inside these games.

Suggested Citation

  • Cerruti, Umberto & Dutto, Simone & Murru, Nadir, 2020. "A symbiosis between cellular automata and genetic algorithms," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    • Handle: RePEc:eee:chsofr:v:134:y:2020:i:c:s0960077920301211
    DOI: 10.1016/j.chaos.2020.109719
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    References listed on IDEAS

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    Cited by:

    1. Contreras-Reyes, Javier E., 2021. "Lerch distribution based on maximum nonsymmetric entropy principle: Application to Conway’s game of life cellular automaton," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    2. Juan Carlos Seck-Tuoh-Mora & Joselito Medina-Marin & Norberto Hernández-Romero & Genaro J. Martínez, 2023. "Mean-Field Analysis with Random Perturbations to Detect Gliders in Cellular Automata," Mathematics, MDPI, vol. 11(20), pages 1-13, October.

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