Pareto-coevolutionary genetic programming for problem decomposition in multi-class classification

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Volumn , Issue , 2007, Pages 464-471

  Lichodzijewski, Peter ^a   Heywood, Malcolm I ^a  

^a DALHOUSIE UNIVERSITY   (Canada)

Author keywords

Classification; Coevolution; Genetic programming; Problem decomposition; Subset selection; Supervised learning; Training efficiency

Indexed keywords

DOMAIN DECOMPOSITION METHODS; EVOLUTIONARY ALGORITHMS; PARETO PRINCIPLE; PATTERN RECOGNITION; SUPERVISED LEARNING;

COEVOLUTION; DATASETS; PROBLEM DECOMPOSITION; SUBSET SELECTION; TRAINING EFFICIENCY;

GENETIC PROGRAMMING;

EID: 34548077217     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1276958.1277058     Document Type: Conference Paper

References (16)

1. D. J. Newman and S. Hettich and C. L. Blake and C. J. Merz. UCI Repository of Machine Learning Databases [http://www.ics.uci.edu/~mlearn/ mlrepository.html]. Irvine, CA: University of California, Dept. of Information and Comp. Science, 1998.
2. S. Hettich and S. D. Bay. The UCI KDD Archive [http://kdd/ics/uci/edu]. Irvine, CA: University of California, Dept. of Information and Comp. Science, 1999.
3. E. Bernado-Mansilla and J. M. Garrell-Guiu. Accuracy-based learning classifier systems: Models, analysis and applications to classification tasks. Evolutionary Computation, 11(3):209-238, 2003.
4. M. Brameier and W. Banzhaf. Evolving teams of predictors with linear genetic programming. Genetic Programming and Evolvable Machines, 2(4):381-407, 2001.
5. M. Brameier and W. Banzhaf. Linear Genetic Programming. Springer, Genetic and Evolutionary Computation Series, 2007.
6. E. D. De Jong and J. B. Pollack. Ideal evaluation from coevolution. Evolutionary Computation, 12:159-192, 2004.
7. C. Elkan. Results of the KDD'99 classifier learning. SIGKDD Explorations, 1(2):63-64, 2000.
8. S. G. Ficici and J. B. Pollack. Pareto optimality in coevolutionary learning. In Proceedings of the 6th European Conference on Advances in Artificial Life, pages 316-325, 2001.
9. G. Folino, C. Pizzuti, and G. Spezzano. Boosting technique for combining cellular GP classifiers. In Proceedings of the European Conference on Genetic Programming, pages 47-67, 2004.
10. J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992.
11. W. B. Langdon and R. Poli. Foundations of Genetic Programming. Springer-Verlag, 2002.
12. M. Lemczyk and M. I. Heywood. Training binary GP classifiers efficiently: A pareto-coevolutionary approach. In Proceedings of the European Conference on Genetic Programming, pages 299-240, 2007.
13. A. R. McIntyre and M. I. Heywood. MOGE: GP classification problem decomposition using multi-objective optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 863-870, 2006.
14. J. Noble and R. A. Watson. Pareto coevolution: Using performance against coevolved opponents in a game as dimensions for pareto selection. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 493-500, 2001.
15. R. A. Watson and J. B. Pollack. Coevolutionary dynamics in a minimal substrate. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 702-709, 2001.
16. S. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2):149-175, 1995.