Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
Adaptively Resizing Populations: An Algorithm and Analysis
Proceedings of the 5th International Conference on Genetic Algorithms
Machine Learning
Evolutionary product unit based neural networks for regression
Neural Networks
JCLEC: a Java framework for evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue (pp 315-357) "Ordered structures in many-valued logic"
IEEE Transactions on Evolutionary Computation
Hybridization of evolutionary algorithms and local search by means of a clustering method
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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 [1], 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.