Classifier systems that compute action mappings
Pier Luca Lanzi, Daniele Loiacono · 2007
The learning in a niche based learning classifier system depends both on the complexity of the problem space and on the number of available actions. In this paper, we introduce a version of XCS with computed actions, briefly XCSCA, that can be applied to problems involving a large number of actions. We report experimental results showing that XCSCA can evolve accurate and compact representations of binary functions which would be challenging for typical learning classifier system models.