A study on learning robustness using asynchronous 1D cellular automata rules

  • Authors:

  • Leonardo Vanneschi;Giancarlo Mauri

  • Affiliations:

  • Department of Informatics, Systems and Communication (D.I.S.Co.), University of Milano-Bicocca, Milan, Italy and ISEGI, Universidade Nova de Lisboa, Lisbon, Portugal 1070-312;Department of Informatics, Systems and Communication (D.I.S.Co.), University of Milano-Bicocca, Milan, Italy

  • Venue:
  • Natural Computing: an international journal
  • Year:
  • 2012

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Abstract

Numerous studies can be found in literature concerning the idea of learning cellular automata (CA) rules that perform a given task by means of machine learning methods. Among these methods, genetic algorithms (GAs) have often been used with excellent results. Nevertheless, few attention has been dedicated so far to the generality and robustness of the learned rules. In this paper, we show that when GAs are used to evolve asynchronous one-dimensional CA rules, they are able to find more general and robust solutions compared to the more usual case of evolving synchronous CA rules.