Accuracy-based Neuro And Neuro-fuzzy Classifier Systems

Learning Classifier Systems traditionally use a binary representation with wildcards added to allow for generalizations over the problem encoding. However, the simple scheme can be limiting in complex domains. In this paper we present results from the use of neural network-based representation schemes within the accuracy-based XCS. Here each rule's condition and action are represented by a small neural network, evolved through the actions of the genetic algorithm. After describing the changes required to the standard production system functionality, optimal performance is presented using multi-layered perceptrons to represent the individual rules. Results from the use of fuzzy logic through radial basis fuction networks are then presented. In particular, the new representation scheme is shown to produce systems where outputs are a function of the inputs.

[1]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[2]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[3]  Nicholas J. Radcliffe,et al.  Genetic Set Recombination , 1992, FOGA.

[4]  J. Doyne Farmer,et al.  A Rosetta stone for connectionism , 1990 .

[5]  Lashon B. Booker,et al.  Improving the Performance of Genetic Algorithms in Classifier Systems , 1985, ICGA.

[6]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[7]  Stewart W. Wilson.  Get Real! XCS with Continuous-Valued Inputs , 1999, Learning Classifier Systems.

[8]  Richard S. Sutton,et al.  Dimensions of Reinforcement Learning , 1998 .

[9]  Richard K. Belew,et al.  Evolving networks: using the genetic algorithm with connectionist learning , 1990 .

[10]  Lawrence Davis,et al.  Mapping neural networks into classifier systems genetic algorithms , 1989 .

[11]  Vasant Honavar,et al.  Evolution and Learning in Radial Basis Function Neural Networks - A Hybrid Approach , 2001 .

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

[13]  黃崇冀,et al.  Machine learning : an artificial intelligence approach , 1988 .

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

[15]  P. Lanzi.  Extending the representation of classifier conditions part I: from binary to messy coding , 1999 .

[16]  Manuel Valenzuela-Rendón,et al.  The Fuzzy Classifier System: A Classifier System for Continuously Varying Variables , 1991, ICGA.

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

[18]  Dale Schuurmans,et al.  Representational Difficulties with Classifier Systems , 1989, ICGA.

[19]  Bruce A. Whitehead,et al.  Evolving space-filling curves to distribute radial basis functions over an input space , 1994, IEEE Trans. Neural Networks.

[20]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[21]  Andrew G. Barto,et al.  Reinforcement learning , 1998 .

[22]  Stewart W. Wilson.  Function approximation with a classifier system , 2001 .

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

[24]  Risto Miikkulainen,et al.  Forming Neural Networks Through Efficient and Adaptive Coevolution , 1997, Evolutionary Computation.

[25]  Boriana L. Milenova,et al.  Fuzzy and neural approaches in engineering , 1997 .

[26]  Andrea Bonarini,et al.  An Introduction to Learning Fuzzy Classifier Systems , 1999, Learning Classifier Systems.

[27]  Luca Lanzi Pier,et al.  Extending the Representation of Classifier Conditions Part II: From Messy Coding to S-Expressions , 1999 .

[28]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[29]  Larry Bull,et al.  A Genetic Programming-based Classifier System , 1999, GECCO.

[30]  Lashon B. Booker,et al.  Triggered Rule Discovery in Classifier Systems , 1989, ICGA.