The Evolutionary Design of Collective Computation in Cellular Automata
The Evolutionary Design of Collective Computation in Cellular Automata
复制标题
元胞自动机集体计算的进化设计
DOI：
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发表时间：
1998
期刊：
影响因子：
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通讯作者：
Rajarshi Das
中科院分区：
文献类型：
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作者：
J. Crutchfield;Melanie Mitchell;Rajarshi Das
我们调查的遗传算法的能力，设计执行计算的元胞自动机。由此产生的细胞自动机的计算策略可以理解使用的框架中，“粒子”嵌入在时空配置携带信息和粒子之间的相互作用的影响信息处理。这种结构分析也可以用来解释遗传算法设计策略的进化过程。更一般地说，我们的目标是了解机器学习过程如何设计具有复杂集体计算能力的复杂分散系统，并开发严格的框架来理解由此产生的动态系统如何执行计算。发表在Machine Learning Journal上。
We investigate the ability of a genetic algorithm to design cellular automata that perform computations. The computational strategies of the resulting cellular automata can be understood using a framework in which "particles" embedded in space-time configurations carry information and interactions between particles effect information processing. This structural analysis can also be used to explain the evolutionary process by which the strategies were designed by the genetic algorithm. More generally, our goals are to understand how machine-learning processes can design complex decentralized systems with sophisticated collective computational abilities and to develop rigorous frameworks for understanding how the resulting dynamical systems perform computation. Subitted to Machine Learning Journal.