Rule Generation by Search Space Division Ining Method using Genetic Algorithms


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 5, No. 11, pp. 2897-2907, Nov. 1998
10.3745/KIPSTE.1998.5.11.2897,   PDF Download:

Abstract

The production-rule generation from training examples is a hard problem that has large search space and many local optimal solutions. Many learning methods are proposed for production-rule generation and genetic algorithms is an alternative learning method. However, traditional genetic algorithms has been known to have and obstacle in converging at the global solution area and show poor efficiency of production-rules generated. In this paper, we propose a production-rule generating method which uses genetic algorithm learning. By analyzing optimal sub-solutions captured by genetic algorithm learning, our method takes advantage of its schema structure and thus generates relatively small rule set.


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Cite this article
[IEEE Style]
J. S. Hyun and Y. B. Joo, "Rule Generation by Search Space Division Ining Method using Genetic Algorithms," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 5, no. 11, pp. 2897-2907, 1998. DOI: 10.3745/KIPSTE.1998.5.11.2897.

[ACM Style]
Jang Su Hyun and Yoon Byung Joo. 1998. Rule Generation by Search Space Division Ining Method using Genetic Algorithms. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 5, 11, (1998), 2897-2907. DOI: 10.3745/KIPSTE.1998.5.11.2897.