Saw-Tooth Algorithm Guided by the Variance of Best Individual Distributions for Designing Evolutionary Neural Networks
This paper proposes a diversity generating mechanism for an evolutionary algorithm that determines the basic structure of Multilayer Perceptron (MLP) classifiers and simultaneously estimates the coefficients of the models. We apply a modified version of a recently proposed diversity enhancement mechanism , that uses a variable population size and periodic partial reinitializations of the population in the form of a saw-tooth function. Our improvement on this standard scheme consists of guiding saw-tooth reinitializations by considering the variance of the best individuals in the population, performing the population restart when the difference of variance between two consecutive generations is lower than a percentage of the previous variance. The empirical results over six benchmark datasets show that the proposed mechanism outperforms the standard saw-tooth algorithm. Moreover, results are very promising in terms of classification accuracy, yielding a state-of-the-art performance.
KeywordsEvolutionary algorithm population reinitializations saw-tooth algorithm neural networks
Unable to display preview. Download preview PDF.
- 2.Fogel, L.: Artificial Intelligence through Simulated Evolution, 1st edn. John Wiley & Sons, New York (1996)Google Scholar
- 5.Smith, R.: Adaptively resizing populations: An algorithm and analysis. In: Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 653–653 (1993)Google Scholar
- 6.Cobb, H., Grefenstette, J.: Genetic algorithms for tracking changing environments. In: Proceedings of the 5th International Conf. on Genetic Algorithms, pp. 523–530 (1993)Google Scholar
- 7.Eshelman, L.: The chc adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In: Proc. Foundations Genetic Algorithms-1, pp. 257–266 (1991)Google Scholar
- 8.Goldberg, D.: Sizing populations for serial and parallel genetic algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms, pp. 70–79 (1989)Google Scholar
- 9.Hervás, C., Martínez-Estudillo, F.J., Gutiérrez, P.A.: Classification by means evolutionary product-unit neural networks. In: Proc. of the 2006 International Joint Conference on Neural Networks, Vancouver, Canada, pp. 2834–2842 (2006)Google Scholar
- 10.Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
- 12.Yao, X., Liu, Y.: Evolving artificial neural networks through evolutionary programming. In: Fogel, L., Angeline, P., Bäck, T. (eds.) Proc. of the Fifth Annual Conference on Evolutionary Programming V, pp. 257–266 (1996)Google Scholar
- 13.Martínez-Estudillo, A.C., Hervás-Martínez, C., Martínez-Estudillo, F.J., García-Pedrajas, N.: Hybridization of evolutionary algorithms and local search by means of a clustering method. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36(3), 534–545 (2006)CrossRefGoogle Scholar
- 15.Ventura, S., Romero, C., Zafra, A., Delgado, J., Hervás, C.: JCLEC: a java framework for evolutionary computation. Soft Computing (published online, 2007)Google Scholar