Natural coding: A more efficient representation for evolutionary learning

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

Volumn 2723, Issue , 2003, Pages 979-990

  Giráldez, Raúl ^a   Aguilar Ruiz, Jesús S ^a   Riquelme, José C ^a  

^a UNIVERSITY OF SEVILLE   (Spain)

Author keywords

Coding; Evolutionary Algorithms; Supervised Learning

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CALCULATIONS; CODES (SYMBOLS); LEARNING SYSTEMS; SUPERVISED LEARNING;

CODING; DECISION RULES; EVOLUTIONARY LEARNING; GENETIC POPULATION; KNOWLEDGE MODEL; LEARNING PROCESS; NATURAL CODING; UCI MACHINE LEARNING REPOSITORY;

EVOLUTIONARY ALGORITHMS;

EID: 26744460495     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45105-6_109     Document Type: Article

References (23)

1. Jesus S. Aguilar-Ruiz and J.C. Riquelme. Improving the Evolutionary Coding for Machine Learning Tasks. European Conference on Artificial Intelligence, ECAI'02, pp. 173-177, IOS Press, Lyon, France, 2002.
2. Jesus S. Aguilar-Ruiz, J.C. Riquelme and M. Toro. Evolutionary Learning of Hierarchical Decision Rules. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, ISSN: 1083-4419, vol. 33, no. 2, pp. 324-331, 2003.
3. J. Antonisse. A new interpretation of schema notation that overturns the binary encoding constraint. In: Third International Conference on Genetic Algorithms, pp. 86-97, 1989.
4. v. Complex Systems 8, pp. 227-256, 1994.
5. C. Blake and E. K. Merz. UCI repository of machine learning databases, 1998.
6. P. Clark and R. Boswell. The cn2 induction algorithm. Machine Learning, vol. 3, no. 4, pp. 261-283, 1989.
7. P. Clark and R. Boswell. Rule induction with cn2: Some recents improvements. Machine Learning: Proceedings of EWSL-91, pp. 51-163, 1991.
8. K. A. DeJong, W. M. Spears, and D. F. Gordon. Using genetic algorithms for concept learning. Machine Learning, 1(13):161-188, 1993.
9. R. Giráldez, Jesus S. Aguilar-Ruiz and J.C. Riquelme. Discretization Oriented to Decision Rule Generation International Conference on Knowledge-Based Intelligent Information & Engineering Systems, KES'02 IOS Press, pp. 275-279 September 16-18, Crema, Italy, 2002.
10. D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989.
11. C. Z. Janikow. A knowledge-intensive genetic algorithm for supervised learning. Machine Learning, 1(13):169-228, 1993.
12. G. Koehler, S. Bhattacharyya and M. Vose. General cardinality genetic algorithms. Evolutionary Computation 5(4), 439-459, 1998.
13. R. S. Michalski, I. Mozetic, J. Hong, and N. Lavrac. The aq15 inductive learning system: An overview and experiments. In Proceedings of the American Association for Artificial intelligence Conference (AAAI), 1986.
14. T. Mitchell. Machine Learning. McGraw Hill, 1997.
15. S. K. Murthy, S. Kasif, and S. Salzberg. A system for induction of oblique decision trees. Journal of Artificial Intelligence Research, 1994.
16. J. R. Quinlan. C4.5: Programs for Machine Learning., Morgan Kaufmann, San Mateo, California, 1993.
17. J. R. Quinlan. See5.0 (http://www.rulequest.com), 1998-2001.
18. N. J. Radcliffe. Genetic Neural Networks on MIMD Computers. Ph. d., University of Edinburgh, 1990.
19. G. Syswerda. Uniform crossover in genetic algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms, pp. 2-9, 1989.
20. G. Venturini. SIA: a supervised inductive algorithm with genetic search for learning attributes based concepts. In Proceedings of European Conference on Machine Learning, pp. 281-296, 1993.
21. M. Vose and A. Wright. The simple genetic algorithm and the Walsh transform: Part I, theory. Evolutionary Computation 6(3), pp. 253-273, 1998.
22. M. Vose and A. Wright. The simple genetic algorithm and the Walsh transform: Part II, the inverse. Evolutionary Computation 6(3), pp. 275-289, 1998.
23. D. R. Wilson and T. R. Martinez. Improved Heterogeneous Distance Functions. Journal of Artificiall Intelligence Research 6(1), pp. 1-34, 1997.