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.

Vanneschi, L., Mauri, G. (2012). A Study on Learning Robustness using Asynchronous 1D Cellular Automata Rules. NATURAL COMPUTING, 11(2), 289-302 [10.1007/s11047-012-9311-3].

A Study on Learning Robustness using Asynchronous 1D Cellular Automata Rules

VANNESCHI, LEONARDO;MAURI, GIANCARLO
2012

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.
Articolo in rivista - Articolo scientifico
Cellular automata; Machine learning; Genetic algorithms
English
2012
11
2
289
302
none
Vanneschi, L., Mauri, G. (2012). A Study on Learning Robustness using Asynchronous 1D Cellular Automata Rules. NATURAL COMPUTING, 11(2), 289-302 [10.1007/s11047-012-9311-3].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/32364
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