15th European Conference on Artificial Intelligence
|July 21-26 2002 Lyon France|
Jesus S. Aguilar-Ruiz, Jose C. Riquelme, Carmelo Del Valle
The most influential factors in the quality of the solutions found by an evolutionary algorithm are a correct coding of the search space and an appropriate evaluation function of the potential solutions. The coding of the search space for the obtaining of decision rules is approached, i.e., the representation of the individuals of the genetic population. Two new methods for encoding discrete and continuous attributes are presented. Our "natural coding" uses one gene per attribute (continuous or discrete) leading to a reduction in the search space. Genetic operators for this approached natural coding are formally described and the reduction of the size of the search space is analysed for several databases from the UCI machine learning repository.
Keywords: Genetic Algorithms, Machine Learning
Citation: Jesus S. Aguilar-Ruiz, Jose C. Riquelme, Carmelo Del Valle: Improving the Evolutionary Coding for Machine Learning Tasks. In F. van Harmelen (ed.): ECAI2002, Proceedings of the 15th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2002, pp.173-177.