This work presents SimBa-2, an improved version of a novel crossover specifically adapted to the evolutionary optimization of neural network designs that aims at overcoming one of the major problems of recombination, known as the permutation problem. The crossover is based on a so-called ‘local similarity’ between two individuals selected for the recombination process from the population, and it is applied according to a similarity threshold. An approach exploiting this operator has been implemented and applied to five benchmark classification problems in machine learning, chosen among some of the well known classification problems provided by the UCI Machine Learning Repository. The application of different similarity threshold values has been investigated and the experimental results show how the behavior of the operator changes with respect to this parameter.
SimBa-2 : improving a novel similarity-based crossover for the evolution of artificial neural networks / A. Azzini, A.G.B. Tettamanzi, M. Dragoni - In: Proceedings of the 2011 11. International conference on intelligent systems design and applications : 22–24 november 2011 : Córdoba, Spain / [a cura di] S. Ventura, A. Abraham, K. Cios, C. Romero, F. Marcelloni, J. M. Benitez, E. Gibaja. - Piscataway : Institute of electrical and electronics engineers, 2011. - ISBN 9781457716751. - pp. 374-379 (( Intervento presentato al 11. convegno International Conference on Intelligent Systems Design and Applications (ISDA) tenutosi a Córdoba, Spain nel 2011 [10.1109/ISDA.2011.6121684].
SimBa-2 : improving a novel similarity-based crossover for the evolution of artificial neural networks
A. AzziniPrimo
;A.G.B. TettamanziSecondo
;
2011
Abstract
This work presents SimBa-2, an improved version of a novel crossover specifically adapted to the evolutionary optimization of neural network designs that aims at overcoming one of the major problems of recombination, known as the permutation problem. The crossover is based on a so-called ‘local similarity’ between two individuals selected for the recombination process from the population, and it is applied according to a similarity threshold. An approach exploiting this operator has been implemented and applied to five benchmark classification problems in machine learning, chosen among some of the well known classification problems provided by the UCI Machine Learning Repository. The application of different similarity threshold values has been investigated and the experimental results show how the behavior of the operator changes with respect to this parameter.
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