Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
A hybrid genetic algorithm for an NP-complete problem with an expensive evaluation function
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
Bounds for the frequency assignment problem
Discrete Mathematics
Artificial intelligence: a new synthesis
Artificial intelligence: a new synthesis
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Mobile Communications Handbook
The Mobile Communications Handbook
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Tabu Search for Frequency Assignment in Mobile Radio Networks
Journal of Heuristics
Using Genetic Algorithms to Solve NP-Complete Problems
Proceedings of the 3rd International Conference on Genetic Algorithms
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A hybrid search algorithm with heuristics for resource allocation problem
Information Sciences—Informatics and Computer Science: An International Journal
Channel assignment schemes for cellular mobile telecommunication systems: A comprehensive survey
IEEE Communications Surveys & Tutorials
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In this paper, an heuristic algorithm is applied to solve the problem of frequency reuse in cellular radiocommunication systems, where the main aim is to obtain a channel assignment free of interferences such that the resulting bandwidth is close to the minimum theoretical channel span required. Specifically, a genetic algorithm (GA) whose probabilities of mutation and crossover are on-line adjusted based on the diversity of the population is presented. This diversity is estimated by means of analyzing the individuals' fitness entropy. The resulting algorithm obtains accurate solutions, thus offering an interesting alternative to other global search techniques, such as simulated annealing, tabu search and neural networks, as well as to standard GAs. A complete selection of the most well-known benchmark instances has been used in order to evaluate the performance of the proposed procedure. Numerical simulations show that optimal bandwidth solutions are achieved within a reasonable computation time for all the problem instances tested.