Ponencia
Improving the Evolutionary Coding for Machine Learning Tasks
Autor/es | Aguilar Ruiz, Jesús Salvador
Riquelme Santos, José Cristóbal Valle Sevillano, Carmelo del |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2002 |
Fecha de depósito | 2020-03-09 |
Publicado en |
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ISBN/ISSN | 978-1-58603-257-9 0922-6389 |
Resumen | 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 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. |
Agencias financiadoras | Comisión Interministerial de Ciencia y Tecnología (CICYT). España |
Identificador del proyecto | TIC1143–C03–02 |
Cita | Aguilar Ruiz, J.S., Riquelme Santos, J.C. y Valle Sevillano, C.d. (2002). Improving the Evolutionary Coding for Machine Learning Tasks. En ECAI 2002: 15th European Conference on Artificial Intelligence (173-177), Lyon, France: IOS Press. |
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