Stochastic Functional Annealing as Optimization Technique: Application to the Traveling Salesman Problem with Recurrent Networks

Authors:
Domingo López-Rodríguez;Enrique Mérida-Casermeiro;Gloria Galán-Marín;Juan M. Ortiz-De-Lazcano-Lobato
Affiliations:
Department of Applied Mathematics, University of Málaga, Málaga, Spain;Department of Applied Mathematics, University of Málaga, Málaga, Spain;Department of Electronics and Electromechanical Engineering, University of Extremadura, Badajoz, Spain;Department of Computer Science and Artificial Intelligence, University of Málaga, Málaga, Spain
Venue:
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Year:
2007

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Abstract

In this work, a new stochastic method for optimization problems is developed. Its theoretical bases guaranteeing the convergence of the method to a minimum of the objective function are presented, by using quite general hypotheses. Its application to recurrent discrete neural networks is also developed, focusing in the multivalued MREM model, a generalization of Hopfield's. In order to test the efficiency of this new method, we study the well-known Traveling Salesman Problem. Experimental results will show that this new model outperforms other techniques, achieving better results, even on average, than other methods.