Training multi layer perceptron network using a genetic algorithm as a global optimizer

Authors:
Heikki Maaranen;Kaisa Miettinen;Marko M. Mäkelä
Affiliations:
Department of Mathematical Information Technology, University of Jyväskylä, P.O. Box 35 (Agora), FIN-40014 University of Jyväskylä, Finland;Department of Mathematical Information Technology, University of Jyväskylä, P.O. Box 35 (Agora), FIN-40014 University of Jyväskylä, Finland;Department of Mathematical Information Technology, University of Jyväskylä, P.O. Box 35 (Agora), FIN-40014 University of Jyväskylä, Finland
Venue:
Metaheuristics
Year:
2004

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Abstract

In this paper, we introduce an approach for solving a regression problem. In regression problems, one tries to reconstruct the original data from a noisy data set. We solve the problem using a genetic algorithm and a neural network called Multi Layer Perceptron (MLP) network. By constructing the neural network in an appropriate way, we are able to form an objective function for the regression problem. We solve the obtained optimization problem using a hybrid genetic algorithm and compare the results to those of a simple multistart method. The hybrid genetic algorithm used is a simple hybridization of a genetic algorithm and a Nelder-Mead simplex method.