Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
Optimal broadcast scheduling in packet radio networks using mean field annealing
IEEE Journal on Selected Areas in Communications
Advanced Communications Technology Satellite (ACTS): four-year system performance
IEEE Journal on Selected Areas in Communications
A gradual neural-network approach for frequency assignment in satellite communication systems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Minimizing interference in satellite communications using transiently chaotic neural networks
Computers & Mathematics with Applications
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
Frequency assignment problem in satellite communications using differential evolution
Computers and Operations Research
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Simultaneous optimization of artificial neural networks for financial forecasting
Applied Intelligence
Evolving distributed resource sharing for cubesat constellations
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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A hybrid Neural-Genetic algorithm (NG) is presented for the frequency assignment problem in satellite communications (FAPSC). The goal of this problem is minimizing the cochannel interference between satellite communication systems by rearranging the frequency assignments. Previous approaches to FAPSC show lack of scalability, which leads to poor results when the size of the problem grows. The NG algorithm consists of a Hopfield neural network which manages the problem constraints hybridized with a genetic algorithm for improving the solutions obtained. This separate management of constraints and optimization of objective function gives the NG algorithm the properties of scalability required.We analyze the FAPSC and its formulation, describe and discuss the NG algorithm and solve a set of benchmark problems. The results obtained are compared with other existing approaches in order to show that the NG algorithm is more scalable and performs better than previous algorithms in the FAPSC.