Exemplar-based Learning Classier System: Towards cargo layout optimization
Hiroyasu Matsushima, Keiki Takadama · 2008
This paper focuses on generalization of learning classifier system (LCS) and explores the method for reducing the time of generalizing conscious rules that have the real number. For this purpose, we pay attention on exemplars (i.e., good examples) and, propose exemplar-based LCS (ECS) that extracts useful exemplars as generalized rules by deleting unnecessary exemplars (some overlapping exemplars) as much as possible. To validate the effectiveness of ECS, this paper applies it to the cargo layout optimization problems. Intensive simulations have revealed the following implications; that (1) the gap between a center of gravity of HTV and its actual center is minimized by ECS in comparison with the other cases that employ 2000 exemplars and the randomly selected exemplars; (2) ECS can minimize the gap with the small numbers of exemplars (i.e., less than 2000 exemplars); and (3) such effectiveness of ECS is maintained even when the predetermined range of the match set is varied, which show the robustness of ECS against the parameter setting.