Evolutionary computation has brought great progress to rule-based learning but this progress is often blind to the optimality of the system design. This work theoretically reveals an optimal learning scheme on the most popular evolutionary rule-based learning approach -the accuracy-based classifier system (or XCS). XCS seeks to form accurate, maximally general rules that together classify the state space of a given domain. Previously, setting up the system to perform well has been a ‘blackart’ as no systematic approach to XCS parameter tuning existed. We derive a theoretical approach that mathematically guarantees that XCS identifies the accurate rules, which also returns a theoretically valid XCS parameter setting. Then, we demonstrate our theoretical setting derives the maximum correctness of rule-identification in the fewest iterations possible. We also experimentally show that our theoretical setting enables XCS to easily solve several challenging problems where it had previously struggled.

中文翻译：

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基于精度的学习分类器系统的学习最优理论

进化计算为基于规则的学习带来了巨大进步，但这种进步往往对系统设计的最优性视而不见。这项工作在理论上揭示了最流行的基于进化规则的学习方法——基于精度的分类器系统（或 XCS）的最佳学习方案。XCS 寻求形成准确的、最通用的规则，这些规则一起对给定域的状态空间进行分类。以前，设置系统以使其性能良好一直是“魔法”，因为不存在 XCS 参数调整的系统方法。我们推导出一种理论方法，在数学上保证 XCS 识别准确的规则，它还返回理论上有效的 XCS 参数设置。然后，我们证明了我们的理论设置可以在尽可能少的迭代中获得最大的规则识别正确性。