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

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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.

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Notes

  1. The number of considered configurations (50) and the number of repetitions performed for the non deterministic update schemes (30) are clearly empirical and they have been chosen because they are the biggest data that allow us to see the results in a “reasonable” amount of time. As it is often the case in experimental studies, the work could be replicated on a more sophisticated hardware and extended using more configurations and repetitions, in order to possibly obtain more precise outcomes.

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Correspondence to Leonardo Vanneschi.

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Vanneschi, L., Mauri, G. A study on learning robustness using asynchronous 1D cellular automata rules. Nat Comput 11, 289–302 (2012). https://doi.org/10.1007/s11047-012-9311-3

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