Abstract
The supervised learning methods applying evolutionary algorithms 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.
This research was supported by the Spanish Research Agency CICYT and European FEDER Funds, under grants TIN2004–00159 and TIN2004–06689–C03–03.
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Giráldez, R., Díaz-Díaz, N., Nepomuceno, I., Aguilar-Ruiz, J.S. (2005). An Approach to Reduce the Cost of Evaluation in Evolutionary Learning. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_98
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DOI: https://doi.org/10.1007/11494669_98
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