Journal of Artificial Evolution and Applications

Volume 2009, Article ID 736398, 25 pages

http://dx.doi.org/10.1155/2009/736398

Review Article

## Learning Classifier Systems: A Complete Introduction, Review, and Roadmap

Department of Genetics, Dartmouth College, Hanover, NH 03755, USA

Received 24 November 2008; Accepted 23 June 2009

Academic Editor: Marylyn Ritchie

Copyright © 2009 Ryan J. Urbanowicz and Jason H. Moore. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Linked References

- J. Holland,
*Hidden Order: How Adaptation Builds Complexity*, Addison-Wesley, Reading, Mass, USA, 1996. - H. H. Dam, H. A. Abbass, C. Lokan, and X. Yao, “Neural-based learning classifier systems,”
*IEEE Transactions on Knowledge and Data Engineering*, vol. 20, no. 1, pp. 26–39, 2008. View at Publisher · View at Google Scholar - J. Holland and J. Reitman, “Cognitive systems based on adaptive agents,” in
*Pattern-Directed Inference Systems*, D. A. Waterman and F. Inand, Eds., Hayes-Roth, 1978. View at Google Scholar - J. H. Holmes, P. L. Lanzi, W. Stolzmann, and S. W. Wilson, “Learning classifier systems: new models, successful applications,”
*Information Processing Letters*, vol. 82, no. 1, pp. 23–30, 2002. View at Publisher · View at Google Scholar · View at MathSciNet - M. Minsky, “Steps toward artificial intelligence,”
*Proceedings of the IRE*, vol. 49, no. 1, pp. 8–30, 1961. View at Google Scholar - J. Holland,
*Adaptation in Natural and Artificial Systems*, University of Michigan Press, Ann Arbor, Mich, USA, 1975. - L. Bull and T. Kovacs,
*Foundations of Learning Classifier Systems*, Springer, Berlin, Germany, 2005. - D. Goldberg,
*Genetic Algorithms in Search, Optimization and Machine Learning*, Addison-Wesley Longman, Boston, Mass, USA, 1989. - O. Sigaud and S. Wilson, “Learning classifier systems: a survey,”
*Soft Computing*, vol. 11, no. 11, pp. 1065–1078, 2007. View at Publisher · View at Google Scholar - J. Holmes, “Learning classifier systems applied to knowledge discovery in clinical research databases,” in
*Learning Classifier Systems, from Foundations to Applications*, pp. 243–262, 2000. View at Google Scholar - D. Goldberg,
*The Design of Innovation: Lessons from and for Competent Genetic Algorithms*, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2002. - P. Langley,
*Elements of Machine Learning*, Morgan Kaufmann, San Francisco, Calif, USA, 1996. - M. B. Harries, C. Sammut, and K. Horn, “Extracting hidden context,”
*Machine Learning*, vol. 32, no. 2, pp. 101–126, 1998. View at Publisher · View at Google Scholar - S. Ben-David, E. Kushilevitz, and Y. Mansour, “Online learning versus offline learning,”
*Machine Learning*, vol. 29, no. 1, pp. 45–63, 1997. View at Google Scholar - R. S. Sutton and A. Barto,
*Reinforcement Learning: An Introduction*, MIT Press, Cambridge, Mass, USA, 1998. - M. Harmon and S. Harmon, “Reinforcement Learning: A Tutorial,” December 1996.
- J. Wyatt, “Reinforcement learning: a brief overview,” in
*Foundations of Learning Classifier Systems*, pp. 179–202, 2005. View at Google Scholar - L. Bull, “Two simple learning classifier systems,” in
*Foundations of Learning Classifier Systems*, pp. 63–89, 2005. View at Google Scholar - S. W. Wilson, “ZCS: a zeroth level classifier system,”
*Evolutionary Computation*, vol. 2, no. 1, pp. 1–18, 1994. View at Google Scholar - D. Goldberg,
*Computer-aided gas pipeline operation using genetic algorithms and machine learning*, Ph.D. thesis, Department Civil Engineering, University of Michigan, Ann Arbor, Mich, USA, 1983. - J. Baker, “Reducing bias and inefficiency in the selection algorithm,” in
*Proceedings of the 2nd International Conference on Genetic Algorithms on Genetic Algorithms and Their Application*, pp. 14–21, 1987. - S. W. Wilson, “Classifier fitness based on accuracy,”
*Evolutionary Computation*, vol. 3, no. 2, pp. 149–175, 1995. View at Google Scholar - S. Smith,
*A learning system based on genetic adaptive algorithms*, Ph.D. thesis, University of Pittsburgh, Pittsburgh, Pa, USA, 1980. - W. Stolzmann, “Anticipatory classifier systems,” in
*Proceedings of the 3rd Annual Genetic Programming Conference*, pp. 658–664, 1998. - J. Holland, “Adaptation,”
*Progress in Theoretical Biology*, vol. 4, pp. 263–293, 1976. View at Google Scholar - G. G. Robertson and R. L. Riolo, “A tale of two classifier systems,”
*Machine Learning*, vol. 3, no. 2-3, pp. 139–159, 1988. View at Publisher · View at Google Scholar - S. Smith, “Flexible learning of problem solving heuristics through adaptive search,” in
*Proceedings of the 8th International Joint Conference on Artificial Intelligence*, pp. 422–425, 1983. - C. Congdon,
*A comparison of genetic algorithms and other machine learning systems of a complex classification task from common disease research*, Ph.D. thesis, University of Michigan, 1995. - R. Riolo,
*Empirical studies of default hierarchies and sequences of rules in learning classifier systems*, Doctoral dissertation, University of Michigan, Ann Arbor, Mich, USA, 1988. - J. Holland, “Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems,”
*Machine Learning*, vol. 2, pp. 593–623, 1986. View at Google Scholar - J. Holland, “A mathematical framework for studying learning in classifier systems,”
*Physica D*, vol. 2, no. 1–3, pp. 307–317, 1986. View at Google Scholar - J. H. Holland, “Adaptive algorithms for discovering and using general patterns in growing knowledge bases,”
*International Journal of Policy Analysis and Information Systems*, vol. 4, no. 3, pp. 245–268, 1980. View at Google Scholar - J. Holland, “Adaptive knowledge acquisition,” unpublished research proposal, 1980.
- J. Holland, “Genetic algorithms and adaptation,” Tech. Rep. 34, Department of Computer and Communication Sciences, University of Michigan, Ann Arbor, Mich, USA, 1981. View at Google Scholar
- J. Holland, “Induction in artificial intelligence,” Tech. Rep., Department of Computer and Communication Sciences, University of Michigan, Ann Arbor, Mich, USA, 1983. View at Google Scholar
- J. Holland, “A more detailed discussion of classifier systems,” Tech. Rep., Department of Computer and Communication Sciences, University of Michigan, Ann Arbor, Mich, USA, 1983. View at Google Scholar
- J. Holland, “Genetic algorithms and adaptation,” in
*Adaptive Control of Ill-Defined Systems*, pp. 317–333, 1984. View at Google Scholar - J. Holland, “Properties of the Bucket brigade,” in
*Proceedings of the 1st International Conference on Genetic Algorithms*, pp. 1–7, 1985. - J. Holland, “A mathematical framework for studying learning in classifier systems,” Research Memo RIS-25r, The Rowland Institute for Science, Cambridge, Mass, USA, 1985. View at Google Scholar
- J. Holland, “Genetic algorithms and classifier systems: foundations and future directions,” in
*Proceedings of the 2nd International Conference on Genetic Algorithms and Their Application*, pp. 82–89, 1987. - A. Samuel, “Some studies in machine learning using the game of checkers,”
*IBM Journal of Research and Development*, pp. 211–232, 1959. View at Google Scholar - L. B. Booker, “Intelligent behavior as an adaptation to the task environment,” 1982. View at Google Scholar
- S. W. Wilson, “Classifier systems and the animat problem,”
*Machine Learning*, vol. 2, no. 3, pp. 199–228, 1987. View at Publisher · View at Google Scholar - S. W. Wilson, “Knowledge growth in an artificial animal,” in
*Proceedings of the 1st International Conference on Genetic Algorithms and Their Application*, pp. 16–23, 1985. - S. W. Wilson and D. Goldberg, “A critical review of classifier systems,” in
*Proceedings of the 3rd International Conference on Genetic Algorithms and Their Application*, pp. 244–255, 1989. - P. Bonelli, A. Parodi, S. Sen, and S. Wilson, “NEWBOOLE: a fast GBML system,” in
*Proceedings of the 7th International Conference on Machine Learning*, pp. 153–159, 1990. - L. Booker, “Triggered rule discovery in classifier systems,” in
*Proceedings of the 3rd International Conference on Genetic Algorithms*, pp. 265–274, 1989. - M. Valenzuela-Rendon, “The fuzzy classifier system: a classifier system for continuously varying variables,” in
*Proceedings of the 4th International Conference on Genetic Algorithm*, pp. 346–353, 1991. - A. Bonarini, “An introduction to learning fuzzy classifier systems,” in
*Proceedings of the International Workshop on Learning Classifier Systems (IWLCS '00)*, vol. 1813 of*Lecture Notes in Artificial Intelligence*, pp. 83–104, 2000. - R. Riolo, “Bucket brigade performance: I. Long sequences of classifiers,” in
*Proceedings of the 2nd International Conference on Genetic Algorithms and Their Application*, pp. 184–195, Lawrence Erlbaum, Mahwah, NJ, USA, 1987. - R. Riolo, “Bucket brigade performance: II. Default hierarchies,” in
*Proceedings of the 2nd International Conference on Genetic Algorithms and Their Application*, pp. 196–201, Lawrence Erlbaum, Mahwah, NJ, USA, 1987. - P. Lanzi and R. Riolo, “Recent trends in learning classifier systems research,” in
*Advances in Evolutionary Computing: Theory and Applications*, Natural Computing Series, pp. 955–988, 2003. View at Google Scholar - A. Barry, “Hierarchy formation within classifier systems: a review,” in
*Proceedings of the 1st International Conference on Evolutionary Algorithms and Their Application (EVCA '96)*, pp. 195–211, 1996. - P. L. Lanzi, “Learning classifier systems: then and now,”
*Evolutionary Intelligence*, vol. 1, no. 1, pp. 63–82, 2008. View at Google Scholar - R. Riolo, “Lookahead planning and latent learning in a classifier system,” in
*Proceedings of the 1st International Conference on Simulation of Adaptive Behavior on from Animals to Animats*, pp. 316–326, 1991. - J. Schaffer and J. Grefenstette, “Multi-objective learning via genetic algorithms,” in
*Proceedings of the 9th International Joint Conference on Artificial Intelligence*, pp. 593–595, 1985. - D. Greene, “Automated knowledge acquisition: overcoming the expert system bottleneck,” in
*Proceedings of the 8th International Conference on Information Systems*, J. DeGross and C. Kriebel, Eds., pp. 107–117, Pittsburgh, Pa, USA, 1987. - J. J. Grefenstette, “Credit assignment in rule discovery systems based on genetic algorithms,”
*Machine Learning*, vol. 3, no. 2-3, pp. 225–245, 1988. View at Publisher · View at Google Scholar - L. B. Booker, “Classifier systems that learn internal world models,”
*Machine Learning*, vol. 3, no. 2-3, pp. 161–192, 1988. View at Publisher · View at Google Scholar - J. Grefenstette, “Incremental learning of control strategies with genetic algorithms,” in
*Proceedings of the 6th International Workshop on Machine Learning*, pp. 340–344, 1989. - J. Grefenstette, “The evolution of strategies for multiagent environments,”
*Adaptive Behavior*, vol. 1, no. 1, p. 65, 1992. View at Google Scholar - J. Grefenstette,
*The User's Guide to SAMUEL-97: An Evolutionary Learning System*, Navy Center for Applied Research in Artificial Intelligence, Naval Research Laboratory, Washington, DC, USA, 1997. - L. Shu and J. Schaeffer, “HCS: adding hierarchies to classifier systems,” in
*Proceedings of the 4th International Conference on Genetic Algorithms and Their Application*, pp. 339–345, 1991. - M. Dorigo and E. Sirtori, “Alecsys: a parallel laboratory for learning classifier systems,” in
*Proceedings of the 4th International Conference on Genetic Algorithms*, 1991. - M. Dorigo, “Alecsys and the autonoMouse: learning to control a real robot by distributed classifier systems,”
*Machine Learning*, vol. 19, no. 3, pp. 209–240, 1995. View at Publisher · View at Google Scholar - K. De Jong and W. Spears, “Learning concept classification rules using genetic algorithms,” in
*Proceedings of the 12th International Conference on Artificial Intelligence (IJCAI '91)*, vol. 2, 1991. - K. A. De Jong, W. M. Spears, and D. F. Gordon, “Using genetic algorithms for concept learning,”
*Machine Learning*, vol. 13, no. 2-3, pp. 161–188, 1993. View at Publisher · View at Google Scholar - C. Janikow,
*Inductive Learning of decision rules in attribute-based examples: a knowledge-intensive genetic algorithm approach*, Ph.D. thesis, University of North Carolina, 1991. - C. Z. Janikow, “A knowledge-intensive genetic algorithm for supervised learning,”
*Machine Learning*, vol. 13, no. 2-3, pp. 189–228, 1993. View at Publisher · View at Google Scholar - D. Greene,
*Inductive knowledge acquisition using genetic adaptive search*, Ph.D. thesis, 1992. - D. P. Greene and S. F. Smith, “Competition-based induction of decision models from examples,”
*Machine Learning*, vol. 13, no. 2-3, pp. 229–257, 1993. View at Publisher · View at Google Scholar - A. Giordana and L. Saitta, “REGAL: an integrated system for learning relations using genetic algorithms,” in
*Proceedings of the 2nd International Workshop on Multistrategy Learning*, pp. 234–249, 1993. - A. Giordana, L. Saitta, and F. Zini, “Learning disjunctive concepts with distributed genetic algorithms,” in
*Proceedings of the 1st IEEE Conference on Evolutionary Computation*, vol. 1, pp. 115–119, Orlando, Fla, USA, June 1994. - A. Giordana and F. Neri, “Search-intensive concept induction,”
*Evolutionary Computation*, vol. 3, no. 4, pp. 375–416, 1995. View at Google Scholar - A. Bonarini, “ELF: learning incomplete fuzzy rule sets for an autonomous robot,” in
*Proceedings of the ELITE Foundation (EUFIT '93)*, pp. 69–75, Aachen, Germany, 1993. - A. Bonarini, “Some methodological issues about designing autonomous agents which learn their behaviors: the ELF experience,” in
*Proceedings of the Cybernetics and Systems Research*, R. Trappl, Ed., pp. 1435–1442, 1994. - A. Bonarini, “Evolutionary learning of fuzzy rules: competition and cooperation,” in
*Fuzzy Modelling: Paradigms and Practice*, pp. 265–284, 1996. View at Google Scholar - D. Cliff and S. Ross, “Adding memory to ZCS,”
*Adaptive Behavior*, vol. 3, no. 2, pp. 101–150, 1994. View at Google Scholar - I. Flockhart and N. Radcliffe, “GA-MINER: parallel data mining with hierarchical genetic algorithms-final report,” EPCC AIKMS GA-Miner-Report 1.
- I. Flockhart, N. Radcliffe, E. Simoudis, J. Han, and U. Fayyad, “A genetic algorithm-based approach to data mining,” in
*Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD '96)*, pp. 299–302, 1996. - J. Holmes, “A genetics-based machine learning approach to knowledge discovery in clinical data,” in
*Proceedings of the AMIA Anual Symposim*, pp. 883–883, 1996. - J. Holmes, “Discovering risk of disease with a learning classifier system,” in
*Proceedings of the 7th International Conference on Genetic Algorithms (ICGA '97)*, pp. 426–433, 1997. - P. Lanzi, “Adding memory to XCS,” in
*Proceedings of IEEE Conference on Evolutionary Computation (ICEC '98)*, pp. 609–614, 1998. - P. Lanzi, “An analysis of the memory mechanism of XCSM,”
*Genetic Programming*, vol. 98, pp. 643–651, 1998. View at Google Scholar - A. Tomlinson and L. Bull, “A corporate classifier system,” Lecture Notes in Computer Science, pp. 550–559, 1998. View at Google Scholar
- A. Tomlinson and L. Bull, “A zeroth level corporate classifier system,” in
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '99)*, pp. 306–313, 1999. - W. Stolzmann, “An introduction to anticipatory classifier systems,” in
*Learning Classifier Systems: From Foundations to Applications*, Lecture Notes in Computer Science, pp. 175–194, 2000. View at Google Scholar - W. Browne,
*The Development of an Industrial Learning Classifier System for Application to a Steel Hot Strip Mill*, Ph.D. thesis, Division of Mechanical Engineering and Energy Studies, University of Wales, Cardiff, UK, 1999. - W. N. L. Browne, K. M. Holford, C. J. Moore, and J. Bullock, “An industrial learning classifier system: the importance of pre-processing real data and choice of alphabet,”
*Engineering Applications of Artificial Intelligence*, vol. 13, no. 1, pp. 25–36, 2000. View at Publisher · View at Google Scholar - P. Lanzi and S. Wilson, “Toward optimal classifier system performance in non-Markov environments,”
*Evolutionary Computation*, vol. 8, no. 4, pp. 393–418, 2000. View at Google Scholar - A. Tomlinson and L. Bull, “A corporate XCS,” in
*Proceedings of the International Workshop on Learning Classifier Systems*, Lecture Notes in Computer Science, pp. 195–208, 2000. - S. W. Wilson, “Get real! XCS with continuous-valued inputs,” in
*Proceedings of the 3rd International Workshop on Advances in Learning Classifier Systems*, Lecture Notes in Computer Science, pp. 209–222, 2000. - D. Walter and C. K. Mohan, “ClaDia: a fuzzy classifier system for disease diagnosis,” in
*Proceedings of the IEEE Conference on Evolutionary Computation (ICEC '00)*, vol. 2, pp. 1429–1435, 2000. - K. Takadama, T. Terano, and K. Shimohara, “Learning classifier systems meet multiagent environments,” in
*Proceedings of the 3rd International Workshop on Learning Classifier Systems (IWLCS '00)*, L. Lanzi, W. Stolzmann, and S. W. Wilson, Eds., pp. 192–210, Springer, 2000. - S. W. Wilson, “Mining oblique data with XCS,” in
*Proceedings of the 3rd International Workshop on Advances in Learning Classifier Systems*, pp. 158–176, 2000. - S. W. Wilson, “Compact rulesets from XCSI,” in
*Proceedings of the 4th International Workshop on Advances in Learning Classifier Systems*, pp. 197–210, 2001. - E. Bernado-Mansilla and J. Garrell-Guiu, “MOLeCS: a multiObjective learning classifier system,” in
*Proceedings of the Conference on Genetic and Evolutionary Computation*, vol. 1, 2000. - E. Mansilla and J. Guiu, “MOLeCS: using multiobjective evolutionary algorithms for learning,” in
*Proceedings of the 1st International Conference on Evolutionary Multi-Criterion Optimization*, Lecture Notes in Computer Science, pp. 696–710, 2001. - P. Gérard and O. Sigaud, “YACS: combining dynamic programming with generalization in classifiersystems,” in
*Proceedings of the 3rd International Workshop on Advances in Learning Classifier Systems (IWLCS '00)*, pp. 52–69, 2000. - P. Gérard, W. Stolzmann, and O. Sigaud, “YACS : a new learning classifier system using anticipation,”
*Soft Computing-A*, vol. 6, no. 3, pp. 216–228, 2002. View at Google Scholar - T. Kovacs,
*A comparison of strength and accuracy-based fitness in learning classifier systems*, Ph.D. thesis, University of Birmingham, Birmingham, UK, 2001. - T. Kovacs, “Two views of classifier systems,” in
*Advances in Learning Classifier Systems*, Lecture Notes in Computer Science, pp. 74–87, 2002. View at Publisher · View at Google Scholar · View at PubMed - M. Butz, “Biasing exploration in an anticipatory learning classifier system,” in
*Proceedings of the 4th International Workshop on Advances in Learning Classifier Systems*, vol. 2321 of*Lecture Notes in Computer Science*, pp. 3–22, 2001. - M. V. Butz and J. Hoffmann, “Anticipations control behavior: animal behavior in an anticipatory learning classifier system,”
*Adaptive Behavior*, vol. 10, no. 2, pp. 75–96, 2002. View at Publisher · View at Google Scholar - S. Picault and S. Landau, “ATNoSFERES: a Darwinian evolutionary model for individual or collective agent behavior,” Tech. Rep., LIP6, Paris, France, 2001. View at Google Scholar
- S. Landau, S. Picault, and A. Drogoul, “ATNoSFERES: a model for evolutive agent behaviors,” in
*Proceedings of the Symposium on Adaptive Agents and Multi-Agent Systems (AISB '01)*, vol. 1, 2001. - S. Landau, S. Picault, O. Sigaud, and P. Gérard, “A comparison between ATNoSFERES and XCSM,” in
*Proceedings of the Genetic and Evolutionary Computation Conference*, pp. 926–933, 2002. - S. Landau, S. Picault, O. Sigaud, and P. Gerard, “Further comparison between ATNoSFERES and XCSM,” in
*Proceedings of the 5th International Workshop on Learning Classifier Systems*, vol. 2661 of*Lecture Notes in Computer Science*, pp. 99–117, 2003. - S. Landau, O. Sigaud, S. Picault, and P. Gerard, “An experimental comparison between ATNoSFERES and ACS,” in
*Proceedings of the International Workshop on Learning Classifier Systems*, vol. 4399 of*Lecture Notes in Computer Science*, pp. 144–160, 2007. - X. Llorà, J. Garrell et al., “Knowledge-independent data mining with fine-grained parallel evolutionary algorithms,” in
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '01)*, pp. 461–468, Morgan Kaufmann, San Francisco, Calif, USA, 2001. - T. Kovacs,
*Genetic based machine learning using fine-grained parallelism for data mining*, Ph.D. thesis, Enginyeria i Arquitectura La Salle, Ramon Llull University, 2002. - X. Llorà and J. Guiu, “Coevolving different knowledge representations with fine-grained parallel learning classifier systems,” in
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '02)*, pp. 934–941, Morgan Kaufmann, San Francisco, Calif, USA, 2002. - S. W. Wilson, “Classifiers that approximate functions,”
*Natural Computing*, vol. 1, no. 2, pp. 211–234, 2002. View at Google Scholar - K. Tharakunnel and D. Goldberg, “XCS with average reward criterion in multi-step environment,” Tech. Rep., Illinois Genetic Algorithms Laboratory (IlliGAL), Department of General Engineering, University of Illinois at Urbana-Champaign, 2002. View at Google Scholar
- J. Hurst, L. Bull, and C. Melhuish, “TCS learning classifier system controller on a real robot,” in
*Proceedings of the 7th International Conference on Parallel Problem Solving from Nature (PPSN '02)*, Lecture Notes in Computer Science, pp. 588–600, Granada, Spain, September 2002. - L. Bull and T. O'Hara, “Accuracy-based neuro and neuro-fuzzy classifier systems,” in
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '02)*, pp. 905–911, 2002. - E. Bernadó-Mansilla and J. M. Garrell-Guiu, “Accuracy-based learning classifier systems: models, analysis and applications to classification tasks,”
*Evolutionary Computation*, vol. 11, no. 3, pp. 209–238, 2003. View at Publisher · View at Google Scholar · View at PubMed - M. Butz and D. Goldberg, “Generalized state values in an anticipatory learning classifier system,” in
*Proceedings of the 7th International Conference on Simulation of Adaptive Behavior in Anticipatory Learning Systems*, Lecture Notes in Computer Science, pp. 282–302, 2003. - M. Butz, K. Sastry, and D. Goldberg, “Tournament selection: stable fitness pressure in XCS,” in
*Proceedings of the Genetic and Evolutionary Computation Conference*, Lecture Notes in Computer Science, pp. 1857–1869, 2003. - X. Llorà and D. E. Goldberg, “Bounding the effect of noise in multiobjective learning classifier systems,”
*Evolutionary Computation*, vol. 11, no. 3, pp. 279–298, 2003. View at Google Scholar - L. Bull, “A simple accuracy-based learning classifier system,” Learning Classifier Systems Group Technical Report UWELCSG03-005, University of the West of England, Bristol, UK, 2003. View at Google Scholar
- P. W. Dixon, D. W. Corne, and M. J. Oates, “A ruleset reduction algorithm for the XCS learning classifier system,” in
*Learning Classifier Systems*, vol. 2661 of*Lecture Notes in Computer Science*, pp. 20–29, 2003. View at Google Scholar - L. Bull, “Lookahead and latent learning in a simple accuracy-based classifier system,” in
*Proceedings of the 8th International Conference on Parallel Problem Solving from Nature*, Lecture Notes in Computer Science, pp. 1042–1050, 2004. - A. Gaspar and B. Hirsbrunner, “PICS: Pittsburgh immune classifier system,” in
*Proceedings of the AISB Symposium on the Immune System and Cognition*, Leeds, UK, March 2004. - A. Gaspar and B. Hirsbrunner, “From optimization to learning in changing environments: the Pittsburgh immune classifier system,” in
*Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS '02)*, September 2002. - J. Hurst and L. Bull, “A self-adaptive neural learning classifier system with constructivism for mobile robot control,” in
*Proceedings of the 8th International Conference on Parallel Problem Solving from Nature (PPSN '04)*, Lecture Notes in Computer Science, pp. 942–951, Birmingham, UK, September 2004. - L. Bull, “A simple payoff-based learning classifier system,” in
*Proceedings of the 8th International Conference on Parallel Problem Solving from Nature*, Lecture Notes in Computer Science, pp. 1032–1041, 2004. - J. Bacardit,
*Pittsburgh genetic-based machine learning in the data mining era: representations, generalization, and run-time*, Ph.D. thesis, Enginyeria i Arquitectura La Salle, Ramon Llull University, Barcelona, European Union, Catalonia, Spain, 2004. - J. Bacardit, “Analysis of the initialization stage of a Pittsburgh approach learning classifier system,” in
*Proceedings of the Conference on Genetic and Evolutionary Computation*, pp. 1843–1850, 2005. - J. Bacardit and M. Butz, “Data mining in learning classifier systems: comparing XCS with GAssist,” in
*Proceedings of the International Workshop on Learning Classifier Systems (IWLCS '07)*, vol. 4399 of*Lecture Notes in Computer Science*, pp. 282–290, 2007. - M. Stout, J. Bacardit, J. D. Hirst, R. E. Smith, and N. Krasnogor, “Prediction of topological contacts in proteins using learning classifier systems,”
*Soft Computing*, vol. 13, no. 3, pp. 245–258, 2009. View at Publisher · View at Google Scholar - P. Gérard, J.-A. Meyer, and O. Sigaud, “Combining latent learning with dynamic programming in the modular anticipatory classifier system,”
*European Journal of Operational Research*, vol. 160, no. 3, pp. 614–637, 2005. View at Publisher · View at Google Scholar - A. Hamzeh and A. Rahmani, “An evolutionary function approximation approach to compute prediction in XCSF,” in
*Proceedings of the 16th European Conference on Machine Learning (ECML '05)*, vol. 3720 of*Lecture Notes in Computer Science*, pp. 584–592, Porto, Portugal, October 2005. View at Publisher · View at Google Scholar - S. Landau, O. Sigaud, and M. Schoenauer, “ATNoSFERES revisited,” in
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '05)*, pp. 1867–1874, 2005. - O. Unold, “Context-free grammar induction with grammar-based classifier system,”
*Archives of Control Science*, vol. 15, no. 4, p. 681, 2005. View at Google Scholar - O. Unold and L. Cielecki, “Grammar-based classifier system,” in
*Issues in Intelligent Systems: Paradigms*, pp. 273–286, EXIT, Warsaw, Poland, 2005. View at Google Scholar - H. Dam, H. Abbass, and C. Lokan, “DXCS: an XCS system for distributed data mining,” in
*Proceedings of the Conference on Genetic and Evolutionary Computation*, pp. 1883–1890, 2005. - H. Dam, H. Abbass, and C. Lokan, “Investigation on DXCS: an XCS system for distribution data mining, with continuous-valued inputs in static and dynamic environments,” in
*Proceedings of the IEEE Cogress on Evolutionary Computation*, 2005. - H. Dam, H. Abbass, and C. Lokan, “The performance of the DXCS system on continuous-valued inputs in stationary and dynamic environments,” in
*Proceedings of the IEEE Congress on Evolutionary Computation*, vol. 1, 2005. - Y. Gao, J. Huang, H. Rong, and D. Gu, “Learning classifier system ensemble for data mining,” in
*Proceedings of the Workshops on Genetic and Evolutionary Computation*, pp. 63–66, 2005. - Y. Gao, L. Wu, and J. Huang, “Ensemble learning classifier system and compact ruleset,” in
*Proceedings of the 6th International Conference Simulated Evolution and Learning (SEAL '06)*, vol. 4247 of*Lecture Notes in Computer Science*, pp. 42–49, Hefei, China, October 2006. - Y. Gao, J. Z. Huang, H. Rong, and D.-Q. Gu, “LCSE: learning classifier system ensemble for incremental medical instances,” in
*Proceedings of the International Workshop on Learning Classifier Systems (IWLCS '07)*, vol. 4399 of*Lecture Notes in Computer Science*, pp. 93–103, 2007. - J. H. Holmes and J. A. Sager, “Rule discovery in epidemiologic surveillance data using EpiXCS: an evolutionary computation approach,” in
*Proceedings of the 10th Conference on Artificial Intelligence in Medicine (AIME '05)*, vol. 3581 of*Lecture Notes in Computer Science*, pp. 444–452, Aberdeen, Scotland, July 2005. - J. H. Holmes and J. A. Sager, “The EpiXCS workbench: a tool for experimentation and visualization,” in
*Proceedings of the International Workshop on Learning Classifier Systems (IWLCS '07)*, vol. 4399 of*Lecture Notes in Computer Science*, pp. 333–344, 2007. - J. H. Holmes, “Detection of sentinel predictor-class associations with XCS: a sensitivity analysis,” in
*Proceedings of the International Workshop on Learning Classifier Systems (IWLCS '07)*, vol. 4399 of*Lecture Notes in Computer Science*, pp. 270–281, 2007. - D. Loiacono and P. Lanzi, “Evolving neural networks for classifier prediction with XCSF,” in
*Proceedings of the Workshop on Evolutionary Computation (ECAI '06)*, pp. 36–40, 2006. - H. Dam, H. Abbass, and C. Lokan, “BCS: a Bayesian learning classifier system,” Tech. Rep. TR-ALAR-200604005, The Artificial Life and Adaptic Robotics Laboratory, School of Information Technology and Electrical Engineering, University of New South Wales, Cardiff, UK, 2006. View at Google Scholar
- J. Bacardit and N. Krasnogor, “Biohel: bioinformatics-oriented hierarchical evolutionary learning (Nottingham ePrints),” Tech. Rep., University of Nottingham, Nottingham, UK, 2006. View at Google Scholar
- J. Bacardit, M. Stout, J. D. Hirst, K. Sastry, X. Llorà, and N. Krasnogor, “Automated alphabet reduction method with evolutionary algorithms for protein structure prediction,” in
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '07)*, pp. 346–353, ACM Press, New York, NY, USA, 2007. View at Publisher · View at Google Scholar - A. Hamzeh and A. Rahmani, “Extending XCSFG beyond linear approximation,” in
*Proceedings of the IEEE Congress on Evolutionary Computation (CEC '06)*, pp. 2246–2253, Vancouver, Canada, July 2006. - A. Hamzeh and A. Rahmani, “A new architecture of XCS to approximate real-valued functions based on high order polynomials using variable-length GA,” in
*Proceedings of the 3rd International Conference on Natural Computation (ICNC '07)*, vol. 3, pp. 515–519, Haikou, China, August 2007. View at Publisher · View at Google Scholar - P. Lanzi and D. Loiacono, “Classifier systems that compute action mappings,” in
*Proceedings of the 9th Genetic and Evolutionary Computation Conference (GECCO '07)*, pp. 1822–1829, ACM Press, New York, NY, USA, 2007. View at Publisher · View at Google Scholar - M. Gershoff and S. Schulenburg, “Collective behavior based hierarchical XCS,” in
*Proceedings of the Conference on Genetic and Evolutionary Computation Conference (GECCO '07)*, pp. 2695–2700, ACM Press, New York, NY, USA, 2007. View at Publisher · View at Google Scholar - R. E. Smith and M. K. Jiang, “MILCS: a mutual information learning classifier system,” in
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '07)*, pp. 2945–2952, ACM Press, New York, NY, USA, 2007. View at Publisher · View at Google Scholar - L. Cielecki and O. Unold, “GCS with real-valued input,” in
*Proceedings of the 2nd International Work-Conference on The Interplay between Natural and Artificial Computation*, vol. 4527 of*Lecture Notes in Computer Science*, pp. 488–497, 2007. - J. Casillas and L. Bull, “Fuzzy-XCS: a Michigan genetic fuzzy system,”
*IEEE Transactions on Fuzzy Systems*, vol. 15, no. 4, pp. 536–550, 2007. View at Publisher · View at Google Scholar - A. Orriols-Puig, J. Casillas, and E. Bernadó-Mansilla, “Fuzzy-UCS: preliminary results,” in
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '07)*, pp. 2871–2874, 2007. View at Publisher · View at Google Scholar - X. Llorà, R. Reddy, B. Matesic, and R. Bhargava, “Towards better than human capability in diagnosing prostate cancer using infrared spectroscopic imaging,” in
*Proceedings of the 9th Genetic and Evolutionary Computation Conference (GECCO '07)*, pp. 2098–2105, ACM Press, New York, NY, USA, 2007. View at Publisher · View at Google Scholar - R. S. Sutton, “Introduction: the challenge of reinforcement learning,”
*Machine Learning*, vol. 8, no. 3-4, pp. 225–227, 1992. View at Publisher · View at Google Scholar - R. S. Sutton, “Learning to predict by the methods of temporal differences,”
*Machine Learning*, vol. 3, no. 1, pp. 9–44, 1988. View at Publisher · View at Google Scholar - C. Watkins, Learning from Delayed Rewards, 1989.
- G. Liepins, M. Hilliard, M. Palmer, and G. Rangarajan, “Alternatives for classifier system credit assignment,” in
*Proceedings of the 11th International Joint Conference on Artificial Intelligence (IJCAI '89)*, pp. 756–761, 1989. - M. Dorigo and H. Bersini, “A comparison of Q-learning and classifier systems,” in
*Proceedings of the 3rd International Conference on Simulation of Adaptive Behavior: From Animals to Animats 3*, pp. 248–255, MIT Press, Cambridge, Mass, USA, 1994. - M. Dorigo, “Genetic and non-genetic operators in ALECSYS,”
*Evolutionary Computation*, vol. 1, no. 2, pp. 151–164, 1993. View at Google Scholar - P. Lanzi and R. Riolo, “A roadmap to the last decade of learning classifier system research(from 1989 to 1999),” in
*Proceedings of the International Workshop on Learning Classifier Systems*, Lecture Notes in Computer Science, pp. 33–61, 2000. - P. W. Frey and D. J. Slate, “Letter recognition using Holland-style adaptive classifiers,”
*Machine Learning*, vol. 6, no. 2, pp. 161–182, 1991. View at Publisher · View at Google Scholar - T. Kovacs,
*A comparison of strength and accuracy-based fitness in learning classifier systems*, Ph.D. thesis, University of Birmingham, Birmingham, UK, 2002. - P. Lanzi, “Learning classifier systems from a reinforcement learning perspective,”
*Soft Computing*, vol. 6, no. 3, pp. 162–170, 2002. View at Google Scholar - M. Butz, D. Goldberg, and W. Stolzmann, “Introducing a genetic generalization pressure to the anticipatory classier system: part 1-theoretical approach,” in
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '00)*, 2000. - M. Butz, D. Goldberg, and W. Stolzmann, “Investigating generalization in the anticipatory classifier system,” in
*Proceedings of the 6th International Conference on Parallel Problem Solving from Nature*, Lecture Notes in Computer Science, pp. 735–744, 2000. - M. Butz, D. Goldberg, and W. Stolzmann, “Probability-enhanced predictions in the anticipatory classifier system,” in
*Proceedings of the International Workshop on Learning Classifier Systems (IWLCS '00)*, pp. 37–51, Springer, 2000. - M. Butz,
*Anticipatory Learning Classifier Systems*, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2002. - J. Bacardit and M. Butz, “Data mining in learning classifier systems: comparing XCS with GAssist,” in
*Proceedings of the 7th International Workshop on Learning Classifier Systems (IWLCS '04)*, 2004. - T. Kovacs and P. Lanzi, “A learning classifier systems bibliography,” Tech. Rep., CSR Centre, School of Computer Science Research, University of Birmingham, Birmingham, UK, 1999. View at Google Scholar
- P. Stalph and M. Butz, Documentation of XCSF-Ellipsoids Java plus Visualization.
- M. Butz, “Documentation of XCSFJava 1.1 plus visualization,” MEDAL Report 2007008, 2007. View at Google Scholar
- M. Butz and O. Herbort, “Context-dependent predictions and cognitive arm control with XCSF,” in
*Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation*, pp. 1357–1364, ACM, New York, NY, USA, 2008. - M. Butz, “Combining gradient-based with evolutionary online learning: an introduction to
learning classifier systems,” in
*Proceedings of the 7th International Conference on Hybrid Intelligent Systems (HIS '07)*, pp. 12–17, 2007. - S. Russell, P. Norvig, J. Canny, J. Malik, and D. Edwards,
*Artificial Intelligence: A Modern Approach*, Prentice-Hall, Englewood Cliffs, NJ, USA, 1995. - M. Butz,
*Rule-based Evolutionary Online Learning Systems: A Principled Approach to LCS Analysis And Design*, Springer, Berlin, Germany, 2006. - J. Koza,
*Genetic Programming: On the Programming of Computers by Means of Natural Selection*, MIT Press, Cambridge, Mass, USA, 1992. - M. Dorigo and T. Stützle,
*Ant Colony Optimization*, MIT Press, Cambridge, Mass, USA, 2004. - L. De Castro and J. Timmis,
*Artificial Immune Systems: A New Computational Intelligence Approach*, Springer, New York, NY, USA, 2002. - S. Haykin,
*Neural Networks: A Comprehensive Foundation*, Prentice-Hall, Upper Saddle River, NJ, USA, 1998. - E. Bernado, X. Llorà, and J. Garrell, “XCS and GALE: a comparative study of two learning classifier systems with six other learning algorithms on classification tasks,” in
*Proceedings of the 4th International Workshop on Learning Classifier Systems (IWLCS '01)*, pp. 337–341, 2001. - F. Kharbat, L. Bull, and M. Odeh, “Mining breast cancer data with XCS,” in
*Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation Conference (GECCO '07)*, pp. 2066–2073, 2007. View at Publisher · View at Google Scholar - O. Unold and K. Tuszyński, “Mining knowledge from data using anticipatory classifier system,”
*Knowledge-Based Systems*, vol. 21, no. 5, pp. 363–370, 2008. View at Publisher · View at Google Scholar - C. Blake and C. Merz, “UCI repository of machine learning databases,” 1998. View at Google Scholar
- S. Alayón, J. I. Estévez, J. Sigut, J. L. Sánchez, and P. Toledo, “An evolutionary Michigan recurrent fuzzy system for nuclei classification in cytological images using nuclear chromatin distribution,”
*Journal of Biomedical Informatics*, vol. 39, no. 6, pp. 573–588, 2006. View at Publisher · View at Google Scholar · View at PubMed - O. Unold, “Grammar-based classifier system for recognition of promoter regions,” in
*Proceedings of the 8th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA '07)*, vol. 4431 of*Lecture Notes in Computer Science*, pp. 798–805, 2007. - T. Kovacs,
*Evolving optimal populations with XCS classier systems*, M.S. thesis, School of Computer Science, University of Birmingham, Birmingham, UK, 1996. - T. Kovacs, “What should a classifier system learn?” in
*Proceedings of the Congress on Evolutionary Computation*, vol. 2, 2001. - T. Kovacs, “What should a classifier system learn and how should we measure it?”
*Soft Computing-A*, vol. 6, no. 3, pp. 171–182, 2002. View at Google Scholar - M. Butz and M. Pelikan, “Analyzing the evolutionary pressures in XCS,” in
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '01)*, pp. 935–942, 2001. - M. V. Butz, T. Kovacs, P. L. Lanzi, and S. W. Wilson, “Toward a theory of generalization and learning in XCS,”
*IEEE Transactions on Evolutionary Computation*, vol. 8, no. 1, pp. 28–46, 2004. View at Publisher · View at Google Scholar - J. Bassett and K. De Jong, “Evolving behaviors for cooperating agents,” in
*Proceedings of the 12th International Symposium on Foundations of Intelligent Systems (ISMIS '00)*, vol. 1932 of*Lecture Notes in Computer Science*, pp. 157–165, 2000. - M. Butz and D. Goldberg, “Bounding the population size in XCS to ensure reproductive opportunities,” in
*Proceedings of the Conference on Genetic and Evolutionary Computation*, Lecture Notes in Computer Science, pp. 1844–1856, 2003. - M. Butz, D. Goldberg, P. Lanzi, and K. Sastry, “Bounding the population size to ensure niche support in XCS,” IlliGAl Report 2004033, July 2004. View at Google Scholar
- M. Butz, D. Goldberg, and P. Lanzi, “Bounding learning time in XCS,” in
*Proceedings of Genetic and Evolutionary Computation Conference (GECCO '04)*, pp. 739–750, Seattle,Wash, USA, June 2004. - M. Butz,
*Rule-based evolutionary online learning systems: learning bounds, classification, and prediction*, Ph.D. thesis, 2004. - L. Bull, “Learning classifier systems: a brief introduction,” Applications of Learning Classifier Systems, 2004.
- L. Booker, “Representing attribute-based concepts in a classifier system,”
*Foundations of Genetic Algorithms*, pp. 115–127, 1991. View at Google Scholar - S. Sen, “A tale of two representations,” in
*Proceedings of the 7th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems*, pp. 245–254, Gordon and Breach, 1994. - R. Riolo, “The emergence of coupled sequences of classifiers,” in
*Proceedings of the 3rd International Conference on Genetic Algorithms and Their Application*, pp. 256–264, Morgan Kaufmann, San Francisco, Calif, USA, 1989. - D. Schuurmans and J. Schaeffer,
*Representational Difficulties with Classifier Systems*, Department of Computing Science, University of Alberta, Edmonton, Canada, 1988. - C. Stone and L. Bull, “For real! XCS with continuous-valued inputs,”
*Evolutionary Computation*, vol. 11, no. 3, pp. 299–336, 2003. View at Google Scholar - H. Dam, H. Abbass, and C. Lokan, “Be real! XCS with continuous-valued inputs,” in
*Proceedings of the Workshops on Genetic and Evolutionary Computation*, pp. 85–87, ACM, New York, NY, USA, 2005. - P. Lanzi and S. Wilson, “Using convex hulls to represent classifier conditions,” in
*Proceedings of the 8th Genetic and Evolutionary Computation Conference (GECCO '06)*, vol. 2, pp. 1481–1488, ACM Press, New York, NY, USA, 2006. - M. Butz, “Kernel-based, ellipsoidal conditions in the real-valued XCS classifier system,” in
*Proceedings of the Conference on Genetic and Evolutionary Computation*, pp. 1835–1842, 2005. - M. Butz, P. Lanzi, and S. Wilson, “Hyper-ellipsoidal conditions in XCS: rotation, linear approximation, and solution structure,” in
*Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation*, pp. 1457–1464, ACM, New York, NY, USA, 2006. - L. Booker, “Improving the performance of genetic algorithms in classifier systems,” in
*Proceedings of the 1st International Conference on Genetic Algorithms*, pp. 80–92, Lawrence Erlbaum Associates, Mahwah, NJ, USA, 1985. - D. Mellor, “A first order logic classifier system,” in
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '05)*, pp. 1819–1826, ACM Press, New York, NY, USA, 2005. View at Publisher · View at Google Scholar - P. Lanzi, “Extending the representation of classifier conditions—part I: from binary to messy coding,” in
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '99)*, vol. 1, pp. 337–344, 1999. - P. Tufts, “Dynamic classifiers: genetic programming and classifier systems,” in
*Proceedings of the Genetic Programming*, pp. 114–119, 1995. - M. Ahluwalia, L. Bull, W. Banzhaf et al., “A genetic programming-based classifier system,” in
*Proceedings of the Genetic and Evolutionary Computation Conference*, vol. 1, pp. 11–18, 1999. - P. Lanzi and A. Perrucci, “Extending the representation of classifier conditions—part II: from messy coding to S-expressions,” in
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '99)*, vol. 1, pp. 345–352, 1999. - P. Lanzi, “Mining interesting knowledge from data with the XCS classier system,” in
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '01)*, pp. 958–965, 2001. - P. Lanzi, “An analysis of generalization in XCS with symbolic conditions,” in
*Proceedings of IEEE Congress on Evolutionary Computation (CEC '07)*, pp. 2149–2156, 2007. View at Publisher · View at Google Scholar - L. Bull and J. Hurst, “A neural learning classifier system with self-adaptive constructivism,” in
*Proceedings of the Congress on Evolutionary Computation (CEC '03)*, vol. 2, 2003. - T. O'Hara and L. Bull, “A memetic accuracy-based neural learning classifier system,” in
*Proceedings of IEEE Congress on Evolutionary Computation (CEC '05)*, vol. 3, pp. 2040–2045, 2005. - B. Carse and T. Fogarty, “A fuzzy classifier system using the Pittsburgh approach,” in
*Proceedings of the International Conference on Evolutionary Computation, the 3rd Conference on Parallel Problem Solving from Nature*, Jerusalem, Israel, October 1994. - M. Butz, K. Sastry, and D. Goldberg, “Tournament selection in XCS,” in
*Proceedings of the 5th Genetic and Evolutionary Computation Conference (GECCO '02)*, vol. 1869, 2002. - M. V. Butz, D. E. Goldberg, and K. Tharakunnel, “Analysis and improvement of fitness exploitation in XCS: bounding models, tournament selection, and bilateral accuracy,”
*Evolutionary Computation*, vol. 11, no. 3, pp. 239–277, 2003. View at Publisher · View at Google Scholar · View at PubMed - F. Kharbat, L. Bull, and M. Odeh, “Revisiting genetic selection in the XCS learning classifier system,” in
*Proceedings of the IEEE Congress on Evolutionary Computation*, vol. 3, 2005. - M. V. Butz, K. Sastry, and D. E. Goldberg, “Strong, stable, and reliable fitness pressure in XCS due to tournament selection,”
*Genetic Programming and Evolvable Machines*, vol. 6, no. 1, pp. 53–77, 2005. View at Publisher · View at Google Scholar - B. Widrow and M. E. Hoff, “Adaptive switching circuits,” in
*IRE WESCON Convention Record*, vol. 4, pp. 709–717, 1960. - G. Venturini,
*Apprentissage adaptatif et apprentissage supervise par algorithme genetique*, These de Docteur en Science, Universite de Paris-Sud, Paris, France, 1994. - M. Butz, T. Kovacs, P. Lanzi, and S. Wilson, “How XCS evolves accurate classifiers,” in
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '01)*, pp. 927–934, 2001. - G. Liepins and L. Wang, “Classifier system learning of Boolean concepts,” in
*Proceedings of the 4th International Conference on Genetic Algorithms*, pp. 318–323, Morgan Kaufmann, San Francisco, Calif, USA, 1991. - G. Weiss,
*The Action oriented Bucket Brigade*, Institut für Informatik, 1991. - G. Weiss, “Ah action-oriented perspective of learning in classifier systems,”
*Journal of Experimental and Theoretical Artificial Intelligence*, vol. 8, no. 1, pp. 43–62, 1996. View at Google Scholar - M. V. Butz, D. E. Goldberg, and P. L. Lanzi, “Gradient descent methods in learning classifier systems: improving XCS performance in multistep problems,”
*IEEE Transactions on Evolutionary Computation*, vol. 9, no. 5, pp. 452–473, 2005. View at Publisher · View at Google Scholar - P. Lanzi, M. V. Butz, and D. E. Goldberg, “Empirical analysis of generalization and learning in XCS with gradient descent,” in
*Proceedings of of the 9th Genetic and Evolutionary Computation Conference (GECCO '07)*, pp. 1814–1821, ACM Press, New York, NY, USA, 2007. View at Publisher · View at Google Scholar - J. Drugowitsch and A. M. Barry, “XCS with eligibility traces,” in
*Proceedings of the Conference on Genetic and Evolutionary Computation Conference (GECCO '05)*, pp. 1851–1858, ACM, New York, NY, USA, 2005. View at Publisher · View at Google Scholar - P. Lanzi, D. Loiacono, S. W. Wilson, and D. E. Goldberg, “Prediction update algorithms for XCSF: RLS, Kalman filter, and gain adaptation,” in
*Proceedings of the 8th Genetic and Evolutionary Computation Conference (GECCO '06)*, vol. 2, pp. 1505–1512, ACM Press, 2006. - J. Horn, D. Goldberg, and K. Deb, “Implicit niching in a learning classifier system: nature's way,”
*Evolutionary Computation*, vol. 2, no. 1, pp. 37–66, 1994. View at Google Scholar - J. Horn, D. Goldberg, J. Koza, D. Goldberg, D. Fogel, and R. Riolo, “Natural niching for cooperative learning in classifier systems,” in
*Proceedings of the 1st Annual Conference on Genetic Programming*, pp. 553–564, MIT Press, 1996. - M. V. Butz, M. Pelikan, X. Llorà, and D. E. Goldberg, “Extracted global structure makes local building block processing effective in XCS,” in
*Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '05)*, pp. 655–662, ACM, New York, NY, USA, 2005. View at Publisher · View at Google Scholar - M. V. Butz, M. Pelikan, X. Llorà, and D. E. Goldberg, “Automated global structure extraction for effective local building block processing in XCS,”
*Evolutionary Computation*, vol. 14, no. 3, pp. 345–380, 2006. View at Publisher · View at Google Scholar · View at PubMed - J. Bacardit and N. Krasnogor, “Smart crossover operator with multiple parents for a pittsburgh learning classifier system,” in
*Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation (GECCO '06)*, vol. 2, pp. 1441–1448, ACM Press, New York, NY, USA, 2006. - F. Serendynski, P. Cichosz, and G. Klebus, “Learning classifier systems in multi-agent environments,” in
*Proceedings of the 1st International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GAESIA '95)*, pp. 287–292, 1995. - S. Sen and M. Sekaran, “Multiagent coordination with learning classifier systems,” in
*Proceedings of the Adaption and Learning in Multi-Agent Systems*, Lecture Notes in Computer Science, pp. 218–233, 1996. - L. Bull, M. Studley, T. Bagnall, and I. Whittley, “On the use of rule-sharing in learning classifier system ensembles,” in
*Proceedings of the Congress on Evolutionary Computation (CEC '05)*, vol. 1, 2005. - L. Bull, M. Studley, A. Bagnall, and I. Whittley, “Learning classifier system ensembles with rule-sharing,”
*IEEE Transactions on Evolutionary Computation*, vol. 11, no. 4, pp. 496–502, 2007. View at Publisher · View at Google Scholar - J. Bacardit and N. Krasnogor, “Empirical evaluation of ensemble techniques for a Pittsburgh learning classifier system,” in
*Proceedings of the 9th International Workshop on Learning Classifier Systems (IWLCS '08)*, vol. 4998, pp. 255–268, 2008. View at Publisher · View at Google Scholar - H. H. Dam, P. Rojanavasu, H. A. Abbass, and C. Lokan, “Distributed learning classifier systems,”
*Studies in Computational Intelligence*, vol. 125, pp. 69–91, 2008. View at Publisher · View at Google Scholar - C. Lokan, “Distributed learning classifier systems,” in
*Learning Classifier Systems in Data Mining*, 2008. View at Google Scholar - R. Ranawana and V. Palade, “Multi-classifier systems: review and a roadmap for developers,”
*International Journal of Hybrid Intelligent Systems*, vol. 3, no. 1, pp. 35–61, 2006. View at Google Scholar - E. Bernado-Mansilla, X. Llorà, and I. Traus, “Multiobjective learning classifier systems: an overview,” Tech. Rep., University of Illinois at Urbana Champaign, Urbana, Ill, USA, 2005. View at Google Scholar
- C. Fu and L. Davis, “A modified classifier system compaction algorithm,” in
*Proceedings of the Conference on Genetic and Evolutionary Computation Conference (GECCO '02)*, pp. 920–925, 2002. - M. V. Butz, P. L. Lanzi, and S. W. Wilson, “Function approximation with XCS: hyperellipsoidal conditions, recursive least squares, and compaction,”
*IEEE Transactions on Evolutionary Computation*, vol. 12, no. 3, pp. 355–376, 2008. View at Publisher · View at Google Scholar - J. Holmes, J. Sager, and W. Bilker, “A comparison of three methods for covering missing data in XCS,” in
*Proceedings of the 7th International Workshop on Learning Classifier Systems (IWLCS '04)*, Seattle, Wash, USA, June 2004. - A. Orriols-Puig and E. Bernadó-Mansilla, “Bounding XCS's parameters for unbalanced datasets,” in
*Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation Conference (GECCO '06)*, vol. 2, pp. 1561–1568, ACM, New York, NY, USA, 2006. - H. Dam, K. Shafi, and H. Abbass, “Can evolutionary computation handle large dataset?” Tech. Rep. TR-ALAR-2005070001, 2005, http://seal.itee.adfa.edu.au/~alar/techreps.
- L. Booker, “Classier systems, endogenous fitness, and delayed rewards: a preliminary investigation,” in
*Proceedings of the International Workshop on Learning Classifier Systems (IWLCS '00) in the Joint Workshops of SAB*, 2000. - J. Hurst and L. Bull, “A self-adaptive classifier system,” in
*Proceedings of the 3rd International Workshop on Advances in Learning Classifier Systems*, pp. 70–79, Springer, 2000. - L. Bull and J. Hurst, “Self-adaptive mutation in ZCS controllers,” in
*Proceedings of the Real-World Applications of Evolutionary Computing, EvoWorkshops*, Lecture Notes in Computer Science, pp. 339–346, 2000. - L. Bull, J. Hurst, and A. Tomlinson, “Self-adaptive mutation in classifier system controllers,” in
*From Animals to Animats 6: Proceedings of the 6th International Conference on Simulation of Adaptive Behavior*, MIT Press, 2000. View at Google Scholar - J. Hurst and L. Bull, “A self-adaptive XCS,” in
*Proceedings of the 4th International Workshop on Advances in Learning Classifier Systems*, Lecture Notes in Computer Science, pp. 57–73, 2002. - W. Browne, “Improving Evolutionary Computation Based Data-Mining for the Process Industry: The Importance of Abstraction,” Learning Classifier Systems in Data Mining, 2008.
- D. Goldberg, J. Horn, and K. Deb, “What makes a problem hard for a classifier system?” Tech. Rep., Santa Fe Working Paper, 1992. View at Google Scholar
- L. B. Booker, D. E. Goldberg, and J. Holland, “Classifier systems and genetic algorithms,” in
*Machine Learning: Paradigms and Methods*, pp. 235–282, 1989. View at Google Scholar - J. Holland, L. Booker, M. Colombetti et al., “What is a learning classifier system?” in
*Learning Classifier Systems, from Foundations to Applications*, Lecture Notes in Computer Science, pp. 3–32, 2000. View at Google Scholar - S. W. Wilson, “State of XCS classifier system research,” in
*Proceedings of the 3rd International Workshop on Advances in Learning Classifier Systems*, Lecture Notes in Computer Science, pp. 63–82, 2000. - T. Kovacs, “Learning classifier systems resources,”
*Soft Computing-A*, vol. 6, no. 3, pp. 240–243, 2002. View at Google Scholar - J. Drugowitsch,
*Design and Analysis of Learning Classifier Systems: A Probabilistic Approach*, Springer, Berlin, Germany, 2008.