XCS Classifier System Reliably Evolves Accurate, Complete, and Minimal Representations for Boolean Functions
XCS Classifier System Reliably Evolves Accurate, Complete, and Minimal Representations for Boolean Functions
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XCS 分类器系统可靠地演化出布尔函数的准确、完整和最小表示
DOI：
10.1007/978-1-4471-0427-8_7
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发表时间：
1998
期刊：
影响因子：
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通讯作者：
T. Kovacs
中科院分区：
文献类型：
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作者：
T. Kovacs
威尔逊最近的XCS分类器系统形成了强化学习传统中回报环境的完整映射，这要归功于它基于准确性的适应度。根据威尔逊的泛化假说，跨文化交际具有泛化的趋势。有了XCS最优性假设，我建议XCS系统可以进化出最优种群(表示)；即使用最小可能的非重叠分类器集将所有输入/动作对精确映射到收益预测的种群。使用凝聚技术证明了XCS进化布尔多路复用器问题最优种群的能力，这是一种通过将交叉和变异率设置为零来暂停进化搜索的技术。通过性能统计的自我监控自动触发压缩，并通过自动终止终止整个学习过程。这些技术结合在一起，允许分类器系统在没有任何形式的监督的情况下进化出布尔函数的最佳表示。还提出了一种更复杂但更健壮和有效的技术来获得最优种群，称为子集提取，并与凝聚进行了比较。
Wilson’s recent XCS classifier system forms complete mappings of the payoff environment in the reinforcement learning tradition thanks to its accuracy based fitness. According to Wilson’sGeneralization Hypothesis, XCS has a tendency towards generalization. With theXCS Optimality Hypothesis, I suggest that XCS systems can evolveoptimal populations(representations); populations which accurately map all input/action pairs to payoff predictions using the smallest possible set of non-overlapping classifiers. The ability of XCS to evolve optimal populations for boolean multiplexer problems is demonstrated using condensation, a technique in which evolutionary search is suspended by setting the crossover and mutation rates to zero. Condensation is automatically triggered by self-monitoring of performance statistics, and the entire learning process is terminated byautotermination. Combined, these techniques allow a classifier system to evolve optimal representations of boolean functions without any form of supervision. A more complex but more robust and efficient technique for obtaining optimal populations calledsubset extractionis also presented and compared to condensation.