Classifier Conditions Using Gene Expression Programming

The classifier system XCSF was modified to use gene expression programming for the evolution and functioning of the classifier conditions. The aim was to fit environmental regularities better than is typically possible with conventional rectilinear conditions. An initial experiment approximating a nonlinear oblique environment showed excellent fit to the regularities.

[1]  Stewart W. Wilson,et al.  Learning Classifier Systems, From Foundations to Applications , 2000 .

[2]  Martin J. Oates,et al.  A Ruleset Reduction Algorithm for the XCS Learning Classifier System , 2002, IWLCS.

[3]  John H. Holland,et al.  COGNITIVE SYSTEMS BASED ON ADAPTIVE ALGORITHMS1 , 1978 .

[4]  Sean Luke,et al.  A Comparison of Bloat Control Methods for Genetic Programming , 2006, Evolutionary Computation.

[5]  Donald A. Waterman,et al.  Pattern-Directed Inference Systems , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[7]  Ouen Pinngern,et al.  Towards clustering with XCS , 2007, GECCO '07.

[8]  Stewart W. Wilson Compact Rulesets from XCSI , 2001, IWLCS.

[9]  Martin V. Butz,et al.  Function Approximation With XCS: Hyperellipsoidal Conditions, Recursive Least Squares, and Compaction , 2008, IEEE Transactions on Evolutionary Computation.

[10]  Julian F. Miller,et al.  Genetic and Evolutionary Computation — GECCO 2003 , 2003, Lecture Notes in Computer Science.

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

[12]  David B. Fogel,et al.  Evolutionary Computation: The Fossil Record , 1998 .

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

[14]  Tim Kovacs,et al.  Advances in Learning Classifier Systems , 2001, Lecture Notes in Computer Science.

[15]  Martin V. Butz Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system , 2005, GECCO '05.

[16]  Weimin Xiao,et al.  Prefix Gene Expression Programming , 2005 .

[17]  Larry Bull,et al.  Learning Classifier Systems , 2002, Annual Conference on Genetic and Evolutionary Computation.

[18]  Cândida Ferreira,et al.  Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence) , 2006 .

[19]  John H. Holland,et al.  Cognitive systems based on adaptive algorithms , 1977, SGAR.

[20]  Martin V. Butz,et al.  Rule-Based Evolutionary Online Learning Systems - A Principled Approach to LCS Analysis and Design , 2006, Studies in Fuzziness and Soft Computing.

[21]  Chunsheng Fu,et al.  A Modified Classifier System Compaction Algorithm , 2002, GECCO.

[22]  Stewart W. Wilson Three Architectures for Continuous Action , 2005, IWLCS.

[23]  G. Cowles Studies of Mascarene Island birds: The fossil record , 1987 .

[24]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

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

[26]  Martin V. Butz,et al.  An Algorithmic Description of XCS , 2000, IWLCS.

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

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

[29]  Candida Ferreira Gene expression programming , 2006 .

[30]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[31]  Larry Bull,et al.  Accuracy-based Neuro And Neuro-fuzzy Classifier Systems , 2002, GECCO.

[32]  Stewart W. Wilson,et al.  Using convex hulls to represent classifier conditions , 2006, GECCO '06.

[33]  Cândida Ferreira,et al.  Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence , 2014, Studies in Computational Intelligence.