Abstract
In this paper Evolutionary Algorithms are investigated in the field of Artificial Neural Networks. In particular, the Breeder Genetic Algorithms are compared against Genetic Algorithms in facing contemporaneously the optimization of (i) the design of a neural network architecture and (ii) the choice of the best learning method for nonlinear system identification. The performance of the Breeder Genetic Algorithms is further improved by a fuzzy recombination operator. The experimental results for the two mentioned evolutionary optimization methods are presented and discussed.
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© 1998 Springer-Verlag London
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De Falco, I., Cioppa, A.D., Natale, P., Tarantino, E. (1998). Artificial Neural Networks Optimization by means of Evolutionary Algorithms. In: Chawdhry, P.K., Roy, R., Pant, R.K. (eds) Soft Computing in Engineering Design and Manufacturing. Springer, London. https://doi.org/10.1007/978-1-4471-0427-8_1
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DOI: https://doi.org/10.1007/978-1-4471-0427-8_1
Publisher Name: Springer, London
Print ISBN: 978-3-540-76214-0
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