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 approximates, 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 producing slightly more compact solutions

[1]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[2]  A. A. Mullin,et al.  Principles of neurodynamics , 1962 .

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[5]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[6]  Robert E. Smith,et al.  Is a Learning Classifier System a Type of Neural Network? , 1994, Evolutionary Computation.

[7]  Stewart W. Wilson Classifier Fitness Based on Accuracy , 1995, Evolutionary Computation.

[8]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[9]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[10]  Stewart W. Wilson Generalization in the XCS Classifier System , 1998 .

[11]  Tim Kovacs,et al.  What Makes a Problem Hard for XCS? , 2000, IWLCS.

[12]  Stewart W. Wilson Mining Oblique Data with XCS , 2000, IWLCS.

[13]  M. Pelikán,et al.  Analyzing the evolutionary pressures in XCS , 2001 .

[14]  Martin V. Butz,et al.  An algorithmic description of XCS , 2000, Soft Comput..

[15]  Martin V. Butz,et al.  Analysis and Improvement of Fitness Exploitation in XCS: Bounding Models, Tournament Selection, and Bilateral Accuracy , 2003, Evolutionary Computation.

[16]  Martin V. Butz,et al.  Toward a theory of generalization and learning in XCS , 2004, IEEE Transactions on Evolutionary Computation.

[17]  Stewart W. Wilson Classifier Systems for Continuous Payoff Environments , 2004, GECCO.

[18]  Stewart W. Wilson Classifiers that approximate functions , 2002, Natural Computing.

[19]  Daniele Loiacono,et al.  XCS with computed prediction in multistep environments , 2005, GECCO '05.

[20]  Martin V. Butz,et al.  Strong, Stable, and Reliable Fitness Pressure in XCS due to Tournament Selection , 2005, Genetic Programming and Evolvable Machines.

[21]  Stewart W. Wilson,et al.  XCS with Computable Prediction in Multistep Environments , 2005 .