Abstract
In this paper we analyze the performance of several evolutionary algorithms in the feature and instance selection problem. It is also introduced the ARGEN + AREPO search algorithm which has been tested in the same problem. There is no need to adapt parameters in this genetic algorithm, except the population size. The reported preliminary results show that using this technique in a wrapper model to search data subsets, we can obtain similar accuracy like with the hill-climbers and genetic algorithms models also here presented, but keeping a less amount of data.
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León-Barranco, A., Reyes-García, C.A. (2005). ARGEN + AREPO: Improving the Search Process with Artificial Genetic Engineering. 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_78
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DOI: https://doi.org/10.1007/11494669_78
Publisher Name: Springer, Berlin, Heidelberg
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