Application of a hybrid genetic algorithm to airline crew scheduling
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
A hybrid neural approach to combinatorial optimization
Computers and Operations Research - Special issue: artificial intelligence, evolutionary programming and operations research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Tabu Search for Frequency Assignment in Mobile Radio Networks
Journal of Heuristics
A Genetic Algorithm for Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
A mixed neural-genetic algorithm for the broadcast scheduling problem
IEEE Transactions on Wireless Communications
An efficient evolutionary algorithm for channel resource managementin cellular mobile systems
IEEE Transactions on Evolutionary Computation
A gradual neural network approach for FPGA segmented channelrouting problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Scheduling multiprocessor job with resource and timing constraintsusing neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Static and dynamic channel assignment using neural networks
IEEE Journal on Selected Areas in Communications
Optimal broadcast scheduling in packet radio networks using mean field annealing
IEEE Journal on Selected Areas in Communications
Dynamic resource allocation schemes during handoff for mobile multimedia wireless networks
IEEE Journal on Selected Areas in Communications
A gradual neural-network approach for frequency assignment in satellite communication systems
IEEE Transactions on Neural Networks
Neural techniques for combinatorial optimization with applications
IEEE Transactions on Neural Networks
Computers and Industrial Engineering
On the performance of the LP-guided Hopfield network-genetic algorithm
Computers and Operations Research
Hybrid cross-entropy method/Hopfield neural network for combinatorial optimization problems
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Computers and Industrial Engineering
Fuzzy ARTMAP and hybrid evolutionary programming for pattern classification
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
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This paper presents a portable and scalable approach for a class of constrained combinatorial optimization problems (CCOPs) which requires to satisfy a set of constraints and to optimize and objective function simultaneously. In particular, this paper is focused on the class of CCOPs that admits a representation in terms of a square matrix of constraints C.The algorithm consists of a hybrid neural-genetic algorithm, formed by a Hopfield Neural Network (HNN) which solves the problem's constraints, and a Genetic Algorithm (GA) for optimizing the objective function. This separated management of constraints and optimization procedures makes the proposed algorithm scalable and robust. The portability of the algorithm is given by the fact that the HNN dynamics depends only on the matrix C of constraints.We show these properties of scalability and portability by solving three different CCOPs with our algorithm, the frequency assignment problem in a mobile telecommunications network, the reduction of the interference in satellite systems and the design of FPGAs with segmented channel routing architecture. We compare our results with previous approaches to these problems, obtaining very good results in all of them.