Improving Performance in Size-Constrained Extended Classifier Systems
نویسنده
چکیده
Extended Classifier Systems, or XCS, have been shown to be successful at developing accurate, complete and compact mappings of a problem’s payoff landscape. However, the experimental results presented in the literature frequently utilize population sizes significantly larger than the size of the search space. This resource requirement may limit the range of problem/hardware combinations to which XCS can be applied. In this paper two sets of modifications are presented that are shown to improve performance in small sizeconstrained 6-Multiplexer and Woods-2 problems.
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Modifying XCS for Size-Constrained Systems
Extended Classifier Systems, or XCS, is a soft-computing approach to machine learning in rule-based systems. While XCS has been shown effective in learning accurate, compact and complete mappings of an environment’s payoff landscape, it can require significant resources to do so. This paper presents four modifications that allow XCS to achieve high performance even in highly size-constrained po...
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تاریخ انتشار 2003