15th European Conference on Artificial Intelligence
  July 21-26 2002     Lyon     France  
   

ECAI-2002 Conference Paper

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Improving the Evolutionary Coding for Machine Learning Tasks

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.


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ECAI-2002 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the UniversitÚ Claude Bernard and INSA, Lyon, on behalf of Association Franšaise pour l'Intelligence Artificielle.