CLASSIFIER SYSTEMS AND GENETIC ALGORITHMS
CLASSIFIER SYSTEMS AND GENETIC ALGORITHMS
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DOI：
10.1016/0004-3702(89)90050-7
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发表时间：
1989-09-01
影响因子：
14.4
通讯作者：
HOLLAND, JH
中科院分区：
文献类型：
--
作者：
BOOKER, LB;GOLDBERG, DE;HOLLAND, JH
分类器系统是大规模并行、消息传递、基于规则的系统，通过信用分配（桶旅算法）和规则发现（遗传算法）进行学习。它们通常在表现出以下一个或多个特征的环境中运行：（1）永远新颖的事件，伴随着大量噪声或不相关的数据； (2) 持续的、通常是实时的行动要求； (3) 含蓄或不明确的目标； (4) 稀疏的回报或强化只能通过长动作序列才能获得。分类器系统旨在不断从此类环境中吸收新信息，设计一组相互竞争的假设（表示为规则），而不会显着干扰已经获得的能力。本文回顾了分类器系统的定义、理论和现有应用，将其与其他机器学习技术进行比较，最后讨论了分类器系统的优点、问题和可能的扩展。
Classifier systems are massively parallel, message-passing, rule-based systems that learn through credit assignment (the bucket brigade algorithm) and rule discovery (the genetic algorithm). They typically operate in environments that exhibit one or more of the following characteristics: (1) perpetually novel events accompanied by large amounts of noisy or irrelevant data; (2) continual, often real-time, requirements for action; (3) implicitly or inexactly defined goals; and (4) sparse payoff or reinforcement obtainable only through long action sequences. Classifier systems are designed to absorb new information continuously from such environments, devising sets of competing hypotheses (expressed as rules) without disturbing significantly capabilities already acquired. This paper reviews the definition, theory, and extant applications of classifier systems, comparing them with other machine learning techniques, and closing with a discussion of advantages, problems, and possible extensions of classifier systems.