Smart crossover operator with multiple parents for a pittsburgh learning classifier system

GECCO 2006 - Genetic and Evolutionary Computation Conference

Volumn 2, Issue , 2006, Pages 1441-1448

  Bacardit, Jaume ^a   Krasnogor, Natalio ^a  

^a UNIVERSITY OF NOTTINGHAM   (United Kingdom)

Author keywords

Evolutionary Algorithms; Learning Classifier Systems; Rule Induction; Smart Crossover operator

Indexed keywords

CLASSIFICATION (OF INFORMATION); EVOLUTIONARY ALGORITHMS; HEURISTIC METHODS; LEARNING SYSTEMS; SET THEORY;

LEARNING CLASSIFIER SYSTEMS; RULE INDUCTION; SMART CROSSOVER OPERATOR;

MATHEMATICAL OPERATORS;

EID: 33750272767     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1143997.1144235     Document Type: Conference Paper

References (15)

1. J. Bacardit. Pittsburgh Genetics-Based Machine Learning in the Data Mining era: Representations, generalization, and run-time. PhD thesis, Ramon Llull University, Barcelona, Catalonia, Spain, 2004. http://www.cs.nott.ac.uk/ jqb/publications/thesis.pdf.
2. J. Bacardit. Analysis of the initialization stage of a Pittsburgh approach learning classifier system. In GECCO 2005: Proceedings of the Genetic and Evolutionary Computation Conference, volume 2, pages 1843-1850. ACM Press, 2005.
3. C. Blake, E. Keogh, and C. Merz. UCI repository of machine learning databases, 1998. (www.ics.uci.edu/mlearn/MLRepository.html).
4. M. V. Butz. Rue-based Evolutionary Online Learning Systems: Learning Bounds, Classification and Prediction. PhD thesis, University of Illinois at Urbana-Champaign, 2004.
5. K. A. DeJong, W. M. Spears, and D. F. Gordon. Using genetic algorithms for concept learning. Machine Learning, 13(2/3):161-188, 1993.
6. D. E. Goldberg. The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, 2002.
7. J. J. Grefenstette. Lamarckian learning in multi-agent environments. In Proceedings of the Fourth International Conference on Genetic Algorithms, pages 303-310. Morgan Kaufmann, 1991.
8. G. Harik. Linkage learning via probabilistic modeling in the ecga, 1999.
9. G. R. Harik, F. G. Lobo, and D. E. Goldberg. The compact genetic algorithm. IEEE-EC, 3(4):287, November 1999.
10. P. Larranaga and J. Lozano, editors. Estimation of Distribution Algorithms, A New Tool for Evolutionnary Computation. Genetic Algorithms and Evolutionnary Computation. Kluwer Academic Publishers, 2002.
11. X. Llora, K. Sastry, and D. Goldberg. The compact classifier system: Scalability analysis and first results. In Proceedings of the Congress on Evolutionary Computation 2005. volume 1, pages 596-603. IEEE Press, 2005.
12. M. Pelikan, D. E. Goldberg, and E. Cantú-Paz. BOA: The Bayesian optimization algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference GECCO-99, volume I, pages 525-532. Morgan Kaufmann, 1999.
13. J. Rissanen. Modeling by shortest data description. Automatica, vol. 14:465-471, 1978.
14. M. Stout, J. Bacardit, J. Hirst, N. Krasnogor, and J. Blazewicz. From hp lattice models to real proteins: coordination number prediction using learning classifier systems. In 4th European Workshop on Evolutionary Computation and Machine Learning in Bioinformatics 2006 (to appear), 2006.
15. S. W. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2):149-175, 1995.