Abstract
In this paper we present a new method for hybrid evolutionary algorithms where only a few best individuals are subject to local optimization. Moreover, the optimization algorithm is only applied at specific stages of the evolutionary process. The key aspect of our work is the use of a clustering algorithm to select the individuals to be optimized. The underlying idea is that we can achieve a very good performance if, instead of optimizing many very similar individuals, we optimize just a few different individuals. This approach is less computationally expensive. Our results show a very interesting performance when this model is compared to other standard algorithms. The proposed model is evaluated in the optimization of the structure and weights of product-unit based neural networks.
This work has been financed in part by the TIC2002-04036-C05-02 project of the Spanish Inter-Ministerial Commission of Science and Technology (CICYT) and FEDER funds.
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Martínez-Estudillo, F., Hervás-Martínez, C., Martínez-Estudillo, A., Ortíz-Boyer, D. (2005). Memetic Algorithms to Product-Unit Neural Networks for Regression. 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_11
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DOI: https://doi.org/10.1007/11494669_11
Publisher Name: Springer, Berlin, Heidelberg
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