Designing multilayer perceptrons using a Guided Saw-tooth Evolutionary Programming Algorithm

In this paper, a diversity generating mechanism is proposed for an Evolutionary Programming (EP) algorithm that determines the basic structure of Multilayer Perceptron classifiers and simultaneously estimates the coefficients of the models. We apply a modified version of a saw-tooth diversity enhancement mechanism recently presented for Genetic Algorithms, which 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. The population restarts are performed when the difference of variance between two consecutive generations is lower than a percentage of the previous variance. From the analysis of the results over ten benchmark datasets, it can be concluded that the computational cost of the EP algorithm with a constant population size is reduced by using the original saw-tooth scheme. Moreover, the guided saw-tooth mechanism involves a significantly lower computer time demand than the original scheme. Finally, both saw-tooth schemes do not involve an accuracy decrease and, in general, they obtain a better or similar precision.