Towards generalization by identification-based XCS in multi-steps problem
Towards generalization by identification-based XCS in multi-steps problem
复制标题
通过基于识别的 XCS 在多步骤问题中实现泛化
DOI：
10.1109/nabic.2011.6089622
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
K. Takadama
中科院分区：
文献类型：
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作者：
Masaya Nakata;Fumiaki Sato;K. Takadama
本文扩展了基于精确的学习分类器系统（XC），以通过选择有效的分类器并删除无效的分类器来促进分类器的概括，并称其为基于识别的XCS（IXC）。通过对迷宫问题的密集模拟（迷宫6），已经揭示了以下含义：（1）与XCSG相比，IXC可以得出较少数量的分类器的良好解决方案作为主要的常规XC之一； （2）IXC不仅可以更快地概括分类器，还可以生成对嘈杂环境的强大分类器。
This paper extends an accuracy-based Learning Classifier System (XCS) to promote a generalization of classifiers by selecting effective ones and deleting ineffective ones, and calls it Identification-based XCS (IXCS). Through the intensive simulations of the Maze problem (Maze6), the following implications have been revealed : (1) IXCS can derive good solutions with a fewer number of classifiers in comparison with XCSG as one of the major conventional XCS; and (2) IXCS can not only generalize the classifiers faster but also generate the classifiers that are robust to the noisy environment.