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.
This work has been partially supported by Junta de Andalucía project number P06-TIC-01615.
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López-Rodríguez, D., Mérida-Casermeiro, E., Galán-Marín, G., Ortiz-de-Lazcano-Lobato, J.M. (2007). Stochastic Functional Annealing as Optimization Technique: Application to the Traveling Salesman Problem with Recurrent Networks. In: Hertzberg, J., Beetz, M., Englert, R. (eds) KI 2007: Advances in Artificial Intelligence. KI 2007. Lecture Notes in Computer Science(), vol 4667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74565-5_30
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DOI: https://doi.org/10.1007/978-3-540-74565-5_30
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