Knowledge extraction and problem structure identification in XCS

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3242, Issue , 2004, Pages 1051-1060

  Butz, Martin V ^a   Lanzi, Pier Luca ^b   Llorà, Xavier ^a   Goldberg, David E ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b POLITECNICO DI MILANO   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BOOLEAN FUNCTIONS; DATA MINING; EXTRACTION; LEARNING ALGORITHMS; LEARNING SYSTEMS; STRUCTURE (COMPOSITION);

BUILDING BLOCKES; DEPENDENCY TREES; IMPORTANT FEATURES; KNOWLEDGE EXTRACTION; PROBLEM COMPLEXITY; PROBLEM REPRESENTATION; PROBLEM STRUCTURE; STRUCTURE IDENTIFICATION;

PROBLEM SOLVING;

EID: 33845339480     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30217-9_106     Document Type: Article

References (20)

1. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI (1975) second edition 1992.
2. Goldberg, D.E.: The race, the hurdle and the sweet spot: Lessons from genetic algorithms for the automation of innovation and creativity. In Bentley, P., ed.: Evolutionary design by computers. Morgan Kaufmann, San Francisco, CA (1999) 105-118
3. Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Boston, MA (2002)
4. Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3 (1995) 149-175
5. Bernadó, E., Llorà, X., Garrell, J.M.: XCS and GALE: A comparative study of two learning classifier systems and six other learning algorithms on classification tasks. In Lanzi, P.L., Stolzmann, W., Wilson, S.W., eds.: Advances in learning classifier systems: Fourth international workshop, IWLCS 2001 (LNAI 2321). SpringerVerlag, Berlin Heidelberg (2002) 115-132
6. Bernadó-Mansilla, E., Garrell-Guiu, J.M.: Accuracy-based learning classifier systems: Models, analysis, and applications to classification tasks. Evolutionary Computation 11 (2003) 209-238
7. Dixon, P.W., Corne, D.W., Oates, M.J.: A preliminary investigation of modified XCS as a generic data mining tool. In Lanzi, P.L., Stolzmann, W., Wilson, S.W., eds.: Advances in learning classifier systems: Fourth international workshop, IWLCS 2001 (LNAI 2321). Springer-Verlag, Berlin Heidelberg (2002) 133-150
8. Butz, M.V., Kovacs, T., Lanzi, P.L., Wilson, S.W.: Toward a theory of generalization and learning in XCS. IEEE Transactions on Evolutionary Computation 8 (2004) 28-46
9. Butz, M.V., Goldberg, D.E., Tharakunnel, K.: Analysis and improvement of fitness exploitation in XCS: Bounding models, tournament selection, and bilateral accuracy. Evolutionary Computation 11 (2003) 239-277
10. Wilson, S.W.: Generalization in the XCS classifier system. Genetic Programming 1998: Proceedings of the Third Annual Conference (1998) 665-674
11. Butz, M.V., Goldberg, D.E., Lanzi, P.L.: PAG Learning in XCS. IlliGAL report 2004011, Illinois Genetic Algorithms Laboratory, University of Illinois at UrbanaChampaign (2004)
12. Wilson, S.W.: Compact rulesets from XCSI. In Lanzi, P.L., Stolzmann, W., Wilson, S.W., eds.: Advances in learning classifier systems: Fourth international workshop, IWLCS 2001 (LNAI 2321). Springer-Verlag, Berlin Heidelberg (2002) 196-208
13. Dixon, P.W., Corne, D.W., Oates, M.J.: A ruleset reduction algorithm for the xcs learning classifier system. In Lanzi, P.L., Stolzmann, W., Wilson, S.W., eds.: Proceedings of the Fifth International Workshop on Learning Classifier Systems, IWLCS 2002. Springer-Verlag, Berlin Heidelberg (in press)
14. Llorà, X., Goldberg, D.E.: Wise breeding ga via machine learning techniques for function optimization. Proceedings of the Fifth Genetic and Evolutionary Computation Conference (GECCO-2003) (2003) 1172-1183
15. Butz, M.V., Wilson, S.W.: An algorithmic description of XCS. Soft Computing 6 (2002) 144-153
16. Butz, M.V., Goldberg, D.E., Lanzi, P.L.: Bounding Learning Time in XCS. IlliGAL report 2004003, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign (2004)
17. De Jong, K.A., Spears, W.M.: Learning concept classification rules using genetic algorithms. IJCAI-91 Proceedings of the Twelfth International Conference on Artificial Intelligence (1991) 651-656
18. Butz, M.V., Goldberg, D.E.: Hierarchical Classification Problems Demand Effective Building Block Identification and Processing in LCSs. IlliGAL report 2004017, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign (2004)
19. Harik, G.: Linkage learning via probabilistic modeling in the ecga. IlliGAL report 99010, Illinois Genetic Algorithms Laboratory, University of Illinois at UrbanaChampaign (1999)
20. Pelikan, M., Goldberg, D.E., Lobo, F.: A survey of optimization by building and using probabilistic models. Computational Optimization and Applications 21 (2002) 5-20