The compact classifier system: Scalability analysis and first results

2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings

Volumn 1, Issue , 2005, Pages 596-603

  Llorà, Xavier ^a   Sastry, Kumara ^a   Goldberg, David E ^a  

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

Author keywords

[No Author keywords available]

Indexed keywords

COMPACT CLASSIFICATION SYSTEMS (CCS); DISTRIBUTION ALGORITHMS; PROBABILITY VECTORS; SCALABILITY ANALYSIS;

ENCODING (SYMBOLS); GENETIC ALGORITHMS; PERTURBATION TECHNIQUES; PROBABILITY; SET THEORY; VECTORS;

CLASSIFICATION (OF INFORMATION);

EID: 27144489969     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. S. Baluja. Population-based incremental learning: A method of integrating genetic search based function optimization and competitive learning. Technical Report CMU-CS-94-163, Carnegie Mellon University, 1994.
2. S. Baluja and R. Caruana. Removing the genetics from the standard genetic algorithm. Technical Report CMU-CS-95-141, Carnegie Mellon University, 1995.
3. K. A. De Jong and W. M. Spears. Learning Concept Classification Rules using Genetic Algorithms. In Proceedings of the Twelfth International Conference on Artificial Intelligence IJCAI-91, volume 2, pages 651-656. Morgan Kaufmann, 1991.
4. D. E. Goldberg. Computer-aided gas pipeline operation using genetic algorithms and rule learning. Dissertation Abstracts International, 44(10):3174B, 1983. Doctoral dissertation, University of Michigan.
5. D. E. Goldberg. Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MA, 1989.
6. D. E. Goldberg, B. Korb, and K. Deb. Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems, 3(5):493-530, 1989. (Also IlliGAL Report No. 89003).
7. G. Harik, E. Cantú-Paz, D. E. Goldberg, and B. L. Miller. The gambler's ruin problem, genetic algorithms, and the sizing of populations. Evolutionary Computation, 1(3):231-253, 1999. (Also IlliGAL Report No. 96004).
8. G. Harik, F. Lobo, and D. E. Goldberg. The compact genetic algorithm. Proceedings of the IEEE International Conference on Evolutionary Computation, pages 523-528, 1998. (Also IlliGAL Report No. 97006).
9. J. H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI, 1975.
10. C. Z. Janikow. A Knowledge Intensive Genetic Algorithm for Supervised Learning. Machine Learning, 13:198-228, 1993.
11. P. L. Lanzi. Extending the Representation of Classifier Conditions Part I: From Binary to Messy Coding. In Proceedings of the Genetic and Evolutinary Computation Conference (GECCO'99), pages 337-344. Morgan Kauffmann, 1999.
12. P. L. Lanzi and A. Perruccxi. Extending the Representation of Classifier Conditions Part II: From Messy Coding to S-Expressions. In Proceedings of the Genetic and Evolutinary Computation Conference (GECCO'99), pages 345-352. Morgan Kauffmann, 1999.
13. P. Larranaga and J. A. Lozano, editors. Estimation of Distribution Algorithms. Kluwer Academic Publishers, Boston, MA, 2002.
14. X. Llorà . Genetic Based Machine Learning using Fine-grained Parallelism for Data Mining. PhD thesis, Enginyeria i Arquitectura La Salle. Ramon Llull University, Barcelona, February, 2002.
15. X. Llorà and J.M. Garrell. Knowledge-Independent Data Mining with Fine-Grained Parallel Evolutionary Algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'2001), pages 461-468. Morgan Kaufmann Publishers, 2001.
16. X. Llorà, K. Sastry, and D. E. Goldberg. Binary Rule Encoding Scheme: A Study Using The Compact Classifier System. In International Workshop on Learning Classifier Systems (IWLCS 2005), page in press, 2005.
17. T.M. Mitchell. Machine Learning. McGraw Hill, 1997.
18. H. Mühlenbein. The equation for response to selection and its use for prediction. Evolutionary Computation, 5(3):303-346, 1997.
19. H. Mühlenbein and G. Paaß. From recombination of genes to the estimation of distributions I. Binary parameters. Parallel Problem Solving from Nature, PPSN IV, pages 178-187, 1996.
20. M. Pelikan, F. Lobo, and D. E. Goldberg. A survey of optimization by building and using probabilistic models. Computational Optimization and Applications, 21:5-20, 2002. (Also IlliGAL Report No. 99018).
21. S. W. Wilson. ZCS: A zeroth level classifier system. Evolutionary Computation, 2(1):1-18, 1994.
22. S. W. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2):149-175, 1995.
23. S. W. Wilson. Get real! XCS with continuous valued inputs. Festschrift in Honor of John H. Holland, pages 111-121, 1999.