Conference paper accepted: Enhanced maxcut clustering with multivalued neural networks and functional annealing

The work Enhanced maxcut clustering with multivalued neural networks and functional annealing has been published in ESANN 2006 Proceedings - 14th European Symposium on Artificial Neural Networks, pp. 25–30.

Abstract:

In this work a new algorithm to improve the performance of optimization methods, by means of avoiding certain local optima, is described. Its theoretical bases are presented in a rigorous, but intuitive, way. It has been applied concretely to the case of recurrent neural networks, in particular to MREM, a multivalued recurrent model, that has proved to obtain very good results when dealing with NP-complete combinatorial optimization problems. In order to show its efficiency, the well-known MaxCut problem for graphs has been selected as benchmark. Our proposal outperforms other specialized and powerful techniques, as shown by simulations.

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