Classifier systems that compute action mappings

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. Categories and Subject Descriptors

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Pier Luca Lanzi, Daniele Loiacono
• Keywords: Computed Actions, GECCO 2007, Learning Classifier System, Optimization, Typical Learning Classifier