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Hybrid Cross-Entropy Method/Hopfield Neural Network for Combinatorial Optimization Problems

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Intelligent Data Engineering and Automated Learning - IDEAL 2007 (IDEAL 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4881))

Abstract

This paper presents a novel hybrid algorithm for combinatorial optimization problems based on mixing the cross-entropy (CE) method and a Hopfield neural network. The algorithm uses the CE method as a global search procedure, whereas the Hopfield network is used to solve the constraints associated to the problems. We have shown the validity of our approach in several instance of the generalized frequency assignment problem.

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Editor information

Hujun Yin Peter Tino Emilio Corchado Will Byrne Xin Yao

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© 2007 Springer-Verlag Berlin Heidelberg

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Ortiz-García, E.G., Pérez-Bellido, Á.M. (2007). Hybrid Cross-Entropy Method/Hopfield Neural Network for Combinatorial Optimization Problems. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_116

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  • DOI: https://doi.org/10.1007/978-3-540-77226-2_116

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77225-5

  • Online ISBN: 978-3-540-77226-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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