Abstract
This paper reviews a competent Pittsburgh LCS that automatically mines important substructures of the underlying problems and takes problems that were intractable with first-generation Pittsburgh LCS and renders them tractable. Specifically, we propose a χ-ary extended compact classifier system (χeCCS) which uses (1) a competent genetic algorithm (GA) in the form of χ-ary extended compact genetic algorithm, and (2) a niching method in the form restricted tournament replacement, to evolve a set of maximally accurate and maximally general rules. Besides showing that linkage exist on the multiplexer problem, and that χeCCS scales exponentially with the number of address bits (building block size) and quadratically with the problem size, this paper also explores non-traditional rule encodings. Gene expression encodings, such as the Karva language, can also be used to build χeCCS probabilistic models. However, results show that the traditional ternary encoding 0,1,# presents a better scalability than the gene expression inspired ones for problems requiring binary conditions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Norwell (2002)
Butz, M.V., Lanzi, P.L., Llorà, X., Goldberg, D.E.: Knowledge extraction and problem structure identification in XCS. Parallel Problem Solving from Nature - PPSN VIII 3242, 1051–1060 (2004)
Butz, M.V., Pelikan, M., Llorà, X., Goldberg, D.E.: Extracted global structure makes local building block processing effective in XCS. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, vol. 1, pp. 655–662 (2005)
Pelikan, M., Lobo, F., Goldberg, D.E.: A survey of optimization by building and using probabilistic models. Computational Optimization and Applications 21, 5–20 (2002)
Harik, G., Lobo, F., Goldberg, D.E.: The compact genetic algorithm. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 523–528 (1998) (Also IlliGAL Report No. 97006)
Llorà, X., Sastry, K., Goldberg, D.E.: The Compact Classifier System: Motivation, analysis, and first results. In: Proceedings of the Congress on Evolutionary Computation, vol. 1, pp. 596–603 (2005)
Kovacs, T.: Strength or Accuracy: Credit Assignment in Learning Classifier Systems. Springer, Heidelberg (2003)
de la Ossa, L., Sastry, K., Lobo, F.G.: Extended compact genetic algorithm in C++: Version 1.1. IlliGAL Report No. 2006013, University of Illinois at Urbana-Champaign, Urbana, IL (March 2006)
Sastry, K., Goldberg, D.E.: Probabilistic model building and competent genetic programming. In: Riolo, R.L., Worzel, B. (eds.) Genetic Programming Theory and Practise, pp. 205–220. Kluwer Academic Publishers, Dordrecht (2003)
Harik, G.R.: Finding multimodal solutions using restricted tournament selection. In: Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 24–31 (1995) (Also IlliGAL Report No. 94002)
Wilson, S.W.: Classier Conditions Using Gene Expression Programming. IlliGAL Technical Report No. 2008001, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign (2008)
Ferreira, C.: Gene expression programming: A new algorithm for solving problems. Complex Systems 13(2), 87–129 (2001)
Ferreira, C.: Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. Springer, Heidelberg (2006)
Bernadó-Mansilla, E., Garrell-Guiu, J.M.: MOLeCS: A MultiObjective Learning Classifier System. In: Proceedings of the 2000 Conference on Genetic and Evolutionary Computation, vol. 1, p. 390 (2000)
Bernadó-Mansilla, E., Llorà, X., Traus, I.: MultiObjective Learning Classifier System. In: MultiObjective Machine Learning, pp. 261–288. Springer, Heidelberg (2005)
Harik, G.R., Lobo, F.G., Sastry, K.: Linkage learning via probabilistic modeling in the ECGA. In: Pelikan, M., Sastry, K., Cantú-Paz, E. (eds.) Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications. Springer, Heidelberg (2006) (Also IlliGAL Report No. 99010)
Goldberg, D.E., Korb, B., Deb, K.: Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems 3(5), 493–530 (1989)
Wilson, S.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)
Llorà, X., Sastry, K., Goldberg, D.E., Gupta, A., Lakshmi, L.: Combating user fatigue in iGAs: Partial ordering, support vector machines, and synthetic fitness. In: GECCO 2005: Proceedings of the 2005 conference on Genetic and evolutionary computation, 25-29 June 2005, vol. 2, pp. 1363–1370. ACM Press, Washington (2005)
Sastry, K., Goldberg, D.E.: Designing competent mutation operators via probabilistic model building of neighborhoods. In: Proceedings of the Genetic and Evolutionary Computation Conference, vol. 2, pp. 114–125 (2004) (Also IlliGAL Report No. 2004006)
Goldberg, D.E., Deb, K., Clark, J.H.: Genetic algorithms, noise, and the sizing of populations. Complex Systems 6, 333–362 (1992)
Pelikan, M.: Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithm. Springer, Berlin (2005)
Sastry, K., Abbass, H.A., Goldberg, D.E., Johnson, D.D.: Sub-structural niching in estimation of distribution algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 671–678 (2005)(Also IlliGAL Report No. 2005003)
Mahfoud, S.W.: Population size and genetic drift in fitness sharing. Foundations of Genetic Algorithms 3, 185–224 (1994)
Llorà, X., Sastry, K., Yu, T.L., Goldberg, D.E.: Do not match, inherit: fitness surrogates for genetics-based machine learning techniques. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 1798–1805. ACM, New York (2007)
Lanzi, P.L.: Extending the Representation of Classifier Conditions Part I: From Binary to Messy Coding. In: Proceedings of the Genetic and Evolutinary Computation Conference (GECCO 1999), pp. 337–344. Morgan Kaufmann, San Francisco (1999)
Lanzi, P., Perrucci, A.: Extending the Representation of Classifier Conditions Part II: From Messy Coding to S-Expressions. In: Proceedings of the Genetic and Evolutinary Computation Conference (GECCO 1999), pp. 345–352. Morgan Kaufmann, San Francisco (1999)
Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Llorà, X., Alías, F., Formiga, L., Sastry, K., Goldberg, D.E.: Evaluation consistency in iGAs: User contradictions as cycles in partial-ordering graphs. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2006), vol. 1, pp. 865–868 (2006) (Also as IlliGAL TR No. 2005022)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Llorà, X., Sastry, K., Lima, C.F., Lobo, F.G., Goldberg, D.E. (2008). Linkage Learning, Rule Representation, and the χ-Ary Extended Compact Classifier System. In: Bacardit, J., Bernadó-Mansilla, E., Butz, M.V., Kovacs, T., Llorà, X., Takadama, K. (eds) Learning Classifier Systems. IWLCS IWLCS 2006 2007. Lecture Notes in Computer Science(), vol 4998. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88138-4_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-88138-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88137-7
Online ISBN: 978-3-540-88138-4
eBook Packages: Computer ScienceComputer Science (R0)