Ponencia
An Approach to Reduce the Cost of Evaluation in Evolutionary Learning
Autor/es | Giráldez, Raúl
Díaz Díaz, Norberto Nepomuceno Chamorro, Isabel de los Ángeles Aguilar Ruiz, Jesús Salvador |
Departamento | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos |
Fecha de publicación | 2005 |
Fecha de depósito | 2022-07-20 |
Publicado en |
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ISBN/ISSN | 978-3-540-26208-4 0302-9743 |
Resumen | The supervised learning methods applying evolutionary al gorithms to generate knowledge model are extremely costly in time and
space. Fundamentally, this high computational cost is fundamentally due
to the evaluation ... The supervised learning methods applying evolutionary al gorithms to generate knowledge model are extremely costly in time and space. Fundamentally, this high computational cost is fundamentally due to the evaluation process that needs to go through the whole datasets to assess their goodness of the genetic individuals. Often, this process carries out some redundant operations which can be avoided. In this paper, we present an example reduction method to reduce the computational cost of the evolutionary learning algorithms by means of extraction, storage and processing only the useful information in the evaluation process. |
Agencias financiadoras | Comisión Interministerial de Ciencia y Tecnología (CICYT). España |
Identificador del proyecto | TIN2004–00159
TIN2004–06689–C03–03 |
Cita | Giráldez, R., Díaz Díaz, N., Nepomuceno Chamorro, I.d.l.Á. y Aguilar Ruiz, J.S. (2005). An Approach to Reduce the Cost of Evaluation in Evolutionary Learning. En IWANN 2005: 8th International Work-Conference on Artificial Neural Networks (804-811), Barcelona, España: Springer. |
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