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 Miguel Ortiz-de-Lazcano-Lobato

Published

1 January 2007

Publication details

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), (4667 LNAI), pp. 397-411

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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. © Springer-Verlag Berlin Heidelberg 2007.

Citation

How to cite

D. López-Rodríguez, E. Mérida-Casermeiro, G. Galán-Marín, et al. “Stochastic functional annealing as optimization technique: Application to the traveling salesman problem with recurrent networks”. In: KI 2007: Advances in Artificial Intelligence, 30th Annual German Conference on AI, KI 2007, Osnabrück, Germany, September 10-13, 2007, Proceedings. Ed. by J. Hertzberg, M. Beetz and R. Englert. Vol. 4667 LNAI. Lecture Notes in Computer Science. cited By 0; Conference of 30th Annual German Conference on Artificial Intelligence, KI 2007 ; Conference Date: 10 September 2007 Through 13 September 2007; Conference Code:70942. Osnabruck: Springer Verlag, 2007, pp. 397-411. DOI: 10.1007/978-3-540-74565-5_30. URL: https://doi.org/10.1007/978-3-540-74565-5_30.

BibTeX

<pre><code>

@InProceedings{LopezRodriguez2007a, author = {D. López-Rodríguez and E. Mérida-Casermeiro and G. Galán-Marín and J.M. Ortiz-de-Lazcano-Lobato}, booktitle = {{KI} 2007: Advances in Artificial Intelligence, 30th Annual German Conference on AI, {KI} 2007, Osnabr{"{u}}ck, Germany, September 10-13, 2007, Proceedings}, title = {Stochastic functional annealing as optimization technique: Application to the traveling salesman problem with recurrent networks}, year = {2007}, address = {Osnabruck}, editor = {Joachim Hertzberg and Michael Beetz and Roman Englert}, note = {cited By 0; Conference of 30th Annual German Conference on Artificial Intelligence, KI 2007 ; Conference Date: 10 September 2007 Through 13 September 2007; Conference Code:70942}, pages = {397-411}, publisher = {Springer Verlag}, series = {Lecture Notes in Computer Science}, volume = {4667 LNAI}, 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. © Springer-Verlag Berlin Heidelberg 2007.}, bibsource = {dblp computer science bibliography, https://dblp.org}, biburl = {https://dblp.org/rec/conf/ki/Lopez-RodriguezCMO07.bib}, document_type = {Conference Paper}, doi = {10.1007/978-3-540-74565-5_30}, journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, keywords = {Computational efficiency; Convergence of numerical methods; Finite difference method; Hopfield neural networks; Traveling salesman problem, Discrete neural networks; Stochastic functional annealing; Stochastic methods, Stochastic models}, source = {Scopus}, url = {https://doi.org/10.1007/978-3-540-74565-5_30}, }

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