Dynamic matching range in Exemplar-based Learning Classifier System
Hiroyasu Matsushima, Kiyohiko Hattori, Hiroyuki Satō, Keiki Takadama · 2010
This paper proposes the extended version of Exemplar-based Learning Classifier System (ECS) called DMR-ECS which introduces the basis function for the dynamic matching selection in ECS. In comparison with our previous match selection in ECS, the proposed dynamic match selection in DMR-ECS can control an appropriate range of the match selection automatically to extract the exemplars that cover given problem space. Intensive simulation on the cargo layout problem has revealed that DMR-ECS contributes to not only improving the performance but also reducing the number of the exemplars with an appropriate range of the match selection.