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

被引：0

作者：

Leonardo Vanneschi

Giancarlo Mauri

机构：

[1] University of Milano-Bicocca,Department of Informatics, Systems and Communication (D.I.S.Co.)

[2] ISEGI,undefined

[3] Universidade Nova de Lisboa,undefined

来源：

Natural Computing | 2012年 / 11卷

关键词：

Cellular automata; Machine learning; Genetic algorithms;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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.

引用

页码：289 / 302

页数：13