Hybrid cross-entropy method/Hopfield neural network for combinatorial optimization problems

Authors:
Emilio G. Ortiz-García;Ángel M. Pérez-Bellido
Affiliations:
Department of Signal Theory and Communications, Universidad de Alcalá, Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá, Madrid, Spain
Venue:
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Year:
2007

Citing 9
Cited 0

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
A Taxonomy of Hybrid Metaheuristics

Journal of Heuristics
Metaheuristics in combinatorial optimization: Overview and conceptual comparison

ACM Computing Surveys (CSUR)
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)

The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
A Hybrid Neural-Genetic Algorithm for the Frequency Assignment Problem in Satellite Communications

Applied Intelligence
A portable and scalable algorithm for a class of constrained combinatorial optimization problems

Computers and Operations Research
A new adaptive genetic algorithm for fixed channel assignment

Information Sciences: an International Journal
A tutorial for competent memetic algorithms: model, taxonomy, and design issues

IEEE Transactions on Evolutionary Computation
A hybrid Hopfield network-simulated annealing approach for frequency assignment in satellite communications systems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

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.