Automated Global Structure Extraction for Effective Local Building Block Processing in XCS

Learning Classifier Systems (LCSs), such as the accuracy-based XCS, evolve distributed problem solutions represented by a population of rules. During evolution, features are specialized, propagated, and recombined to provide increasingly accurate subsolutions. Recently, it was shown that, as in conventional genetic algorithms (GAs), some problems require efficient processing of subsets of features to find problem solutions efficiently. In such problems, standard variation operators of genetic and evolutionary algorithms used in LCSs suffer from potential disruption of groups of interacting features, resulting in poor performance. This paper introduces efficient crossover operators to XCS by incorporating techniques derived from competent GAs: the extended compact GA (ECGA) and the Bayesian optimization algorithm (BOA). Instead of simple crossover operators such as uniform crossover or one-point crossover, ECGA or BOA-derived mechanisms are used to build a probabilistic model of the global population and to generate offspring classifiers locally using the model. Several offspring generation variations are introduced and evaluated. The results show that it is possible to achieve performance similar to runs with an informed crossover operator that is specifically designed to yield ideal problem-dependent exploration, exploiting provided problem structure information. Thus, we create the first competent LCSs, XCS/ECGA and XCS/BOA, that detect dependency structures online and propagate corresponding lower-level dependency structures effectively without any information about these structures given in advance.

[1]  Max Henrion,et al.  Propagating uncertainty in bayesian networks by probabilistic logic sampling , 1986, UAI.

[2]  Martin V. Butz,et al.  Rule-Based Evolutionary Online Learning Systems: Learning Bounds, Classification, and Prediction , 2004 .

[3]  D. Goldberg,et al.  BOA: the Bayesian optimization algorithm , 1999 .

[4]  T. Kovacs Deletion schemes for classifier systems , 1999 .

[5]  W. Michael Rudnick Genetic algorithms and fitness variance with an application to the automated design of neural netoworks , 1992 .

[6]  Fernando G. Lobo,et al.  Extended Compact Genetic Algorithm in C , 1999 .

[7]  David E. Goldberg,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .

[8]  Heinz Mühlenbein,et al.  How Genetic Algorithms Really Work: Mutation and Hillclimbing , 1992, PPSN.

[9]  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.

[10]  Wray L. Buntine Theory Refinement on Bayesian Networks , 1991, UAI.

[11]  David E. Goldberg,et al.  Getting the best of both worlds: Discrete and continuous genetic and evolutionary algorithms in concert , 2003, Inf. Sci..

[12]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

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

[14]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[15]  David E. Goldberg,et al.  Bayesian optimization algorithm, decision graphs, and Occam's razor , 2001 .

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

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

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

[19]  E. Reed The Ecological Approach to Visual Perception , 1989 .

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

[21]  Dirk Thierens,et al.  Mixing in Genetic Algorithms , 1993, ICGA.

[22]  A. Martin V. Butz,et al.  The anticipatory classifier system and genetic generalization , 2002, Natural Computing.

[23]  David Maxwell Chickering,et al.  A Bayesian Approach to Learning Bayesian Networks with Local Structure , 1997, UAI.

[24]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.

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

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

[27]  Gregory F. Cooper,et al.  A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .

[28]  G. Harik Linkage Learning via Probabilistic Modeling in the ECGA , 1999 .

[29]  Ronald A. Howard,et al.  Influence Diagrams , 2005, Decis. Anal..

[30]  Pedro Larrañaga,et al.  Optimization in Continuous Domains by Learning and Simulation of Gaussian Networks , 2000 .

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

[32]  David E. Goldberg,et al.  A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..

[33]  David E. Goldberg,et al.  Bayesian Optimization Algorithm: From Single Level to Hierarchy , 2002 .

[34]  Nir Friedman,et al.  Learning Bayesian Networks with Local Structure , 1996, UAI.

[35]  Ronald A. Howard,et al.  Readings on the Principles and Applications of Decision Analysis , 1989 .

[36]  D. Goldberg,et al.  Domino convergence, drift, and the temporal-salience structure of problems , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[37]  Schloss Birlinghoven,et al.  How Genetic Algorithms Really Work I.mutation and Hillclimbing , 2022 .


[39]  Martin Pelikan,et al.  Hierarchical Bayesian optimization algorithm: toward a new generation of evolutionary algorithms , 2010, SICE 2003 Annual Conference (IEEE Cat. No.03TH8734).

[40]  E. Cantu-Paz,et al.  The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations , 1997, Evolutionary Computation.

[41]  Kenneth A. De Jong,et al.  Learning Concept Classification Rules Using Genetic Algorithms , 1991, IJCAI.

[42]  Pier Luca Lanzi,et al.  An Analysis of Generalization in the XCS Classifier System , 1999, Evolutionary Computation.

[43]  T. Kovacs XCS Classifier System Reliably Evolves Accurate, Complete, and Minimal Representations for Boolean Functions , 1998 .

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

[45]  D.E. Goldberg,et al.  Classifier Systems and Genetic Algorithms , 1989, Artif. Intell..

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

[47]  Olivier Sigaud,et al.  Designing Efficient Exploration with MACS: Modules and Function Approximation , 2003, GECCO.

[48]  Martin V. Butz,et al.  How XCS evolves accurate classifiers , 2001 .

[49]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[50]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[51]  Pedro Larrañaga,et al.  A Review on Estimation of Distribution Algorithms , 2002, Estimation of Distribution Algorithms.

[52]  David Maxwell Chickering,et al.  Learning Bayesian networks: The combination of knowledge and statistical data , 1995, Mach. Learn..

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

[54]  Martin V. Butz,et al.  Tournament Selection: Stable Fitness Pressure in XCS , 2003, GECCO.

[55]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[56]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

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

[58]  D. Goldberg,et al.  Bounding the Population Size to Ensure Niche Support in XCS , 2004 .

[59]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[60]  David E. Goldberg,et al.  Real-Coded Bayesian Optimization Algorithm: Bringing the Strength of BOA into the Continuous World , 2004, GECCO.

[61]  D. Goldberg,et al.  Bounding Learning Time in XCS , 2004, GECCO.

[62]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[63]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .