XCS with computed prediction for the learning of Boolean functions

Computed prediction represents a major shift in learning classifier system research. XCS with computed prediction, based on linear approximators, has been applied so far to function approximation, to single step problems involving continuous payoff functions, and to multi step problems. In this paper we take this new approach in a different direction and apply it to the learning of Boolean functions – a domain characterized by highly discontinuous 0/1000 payoff functions. We also extend it to the case of computed prediction based on functions, borrowed from neural networks, that may be more suitable for 0/1000 payoff problems: the perceptron and the sigmoid. The results we present show that XCSF with linear prediction performs optimally in typical Boolean domains and it allows more compact solutions evolving classifiers that are more general compared with XCS. In addition, perceptron based and sigmoid based prediction can converge slightly faster than linear prediction while produc...

• Type: Conference
• Year: 2005
• Where: CEC
• Authors: Pier Luca Lanzi, Daniele Loiacono, Stewart W. Wilson, David E. Goldberg
• Keywords: Artificial Intelligence, CEC 2005, Computed Prediction, Continuous Payoff Functions, Payoff Functions