Using cellular automata with evolutionary learned rules to solve the online partitioning problem
Using cellular automata with evolutionary learned rules to solve the online partitioning problem
复制标题
使用具有进化学习规则的元胞自动机来解决在线划分问题
DOI：
10.1109/cec.2005.1554770
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
Steffen Priesterjahn
中科院分区：
文献类型：
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作者：
Andreas Goebels;Alexander Weimer;Steffen Priesterjahn
在近年来的计算机科学研究中，由自主个体组成的高度鲁棒性和可扩展性的集合变得越来越重要。在线分割问题（online partitioning problem，缩写为IPSO）是指在考虑多个目标的情况下，将大量的代理分配到不同的目标上。智能体只能在本地交互，没有中心实例或全局知识。在本文中，我们的工作在这个问题上的领域修改的想法，从该地区的元胞自动机（CA）。我们扩大了众所周知的大多数/密度分类任务的一维CA的二维CA。通过使用遗传算法（GA）的CA的过渡规则的学习。遗传算法中的每个个体都是一组带有额外距离信息的转换规则。与其他策略相比，这种方法表现出非常好的行为，并且一旦GA学习到适当的规则集，这种方法就非常快
In recent computer science research highly robust and scalable sets that are composed of autonomous individuals have become more and more important. The online partitioning problem (OPP) deals with the distribution of huge sets of agents onto different targets in consideration of several objectives. The agents can only interact locally and there is no central instance or global knowledge. In this paper we work on this problem field by modifying ideas from the area of cellular automata (CA). We expand the well known majority/density classification task for one-dimensional CAs to two-dimensional CAs. The transition rules for the CA are learned by using a genetic algorithm (GA). Each individual in the GA is a set of transition rules with additional distance information. This approach shows very good behaviour compared to other strategies for the OPP and is very fast once an appropriate set of rules is learned by the GA