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Archives-holding XCS Classifier System: A preliminary study

机译:档案保管XCS分类器系统:初步研究

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In dynamic environment, Learning Classifier System (LCS) evolves classifiers to fit the current situation, but may forget classifiers which were useful for previous situations. Our main idea is that, we store the forgotten classifiers as archives and generate new classifiers by recombining them to fit the current situation. Specifically, we propose an archive-based LCS called Arc-XCS, which detects environmental changes and generates classifiers based on the archive. The experimental results on the benchmark problem show that, Arc-XCS successfully stored good classifiers when each environmental changes occurs; compared to the conventional LCS (XCS), Arc-XCS reaches better performances with fewer trainings.
机译:在动态环境中,学习分类器系统(LCS)会根据当前情况发展分类器,但可能会忘记对以前的情况有用的分类器。我们的主要思想是,将被遗忘的分类器存储为档案,并通过重组它们以适应当前情况来生成新的分类器。具体来说,我们提出了一个基于存档的LCS(称为Arc-XCS),它可以检测环境变化并基于存档生成分类器。针对基准问题的实验结果表明,当每次环境变化发生时,Arc-XCS都能成功存储良好的分类器。与传统的LCS(XCS)相比,Arc-XCS只需较少的培训即可达到更好的性能。

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