Evolving Memory: Logical Tasks for Cellular Automata
Evolving Memory: Logical Tasks for Cellular Automata
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进化记忆：元胞自动机的逻辑任务
DOI：
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
Luis Mateus Rocha
中科院分区：
文献类型：
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作者：
Luis Mateus Rocha
我们提出了新的细胞自动机（CA）的进化实验，以解决非平凡的任务。使用遗传算法，我们进化出了CA规则，可以解决与文献中常用的密度任务（或多数分类问题）相关的非平凡逻辑任务。在计算力学框架下给出了新规则的粒子目录。我们从Crutchfield等人（2002）的研究中了解到，CA中的粒子计算是一个信息处理和集成的过程。在这里，我们讨论的类型的记忆，从不断发展的CA实验中出现的存储和操纵信息。特别是，我们将这种类型的进化记忆与我们在计算机科学中熟悉的记忆类型以及由DNA实例化的生物记忆类型进行了对比。从我们自己的实验中获得的一种新的CA规则被用来阐明一维CA可以达到的记忆类型。
We present novel experiments in the evolution of Cellular Automata (CA) to solve nontrivial tasks. Using a genetic algorithm, we evolved CA rules that can solve non-trivial logical tasks related to the density task (or majority classification problem) commonly used in the literature. We present the particle catalogs of the new rules following the computational mechanics framework. We know from Crutchfield et al (2002) that particle computation in CA is a process of information processing and integration. Here, we discuss the type of memory that emerges from the evolving CA experiments for storing and manipulating information. In particular, we contrast this type of evolved memory with the type of memory we are familiar with in Computer Science, and also with the type of biological memory instantiated by DNA. A novel CA rule obtained from our own experiments is used to elucidate the type of memory that one-dimensional CA can attain.