Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Journal of the ACM (JACM)
A comparison of local search algorithms for radio link frequency assignment problems
SAC '96 Proceedings of the 1996 ACM symposium on Applied Computing
The Frequency Assignment Problem: A Look at the Performance of Evolutionary Search
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Solving Graph Partitioning Problem Using Genetic Algorithms
MWSCAS '98 Proceedings of the 1998 Midwest Symposium on Systems and Circuits
Proceedings of the sixteenth annual ACM symposium on Parallelism in algorithms and architectures
A comparison of problem decomposition techniques for the FAP
Journal of Heuristics
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
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The FAP is an optimisation problem which assigns frequencies to ransmitters in as efficient way as possible, either in terms of interference, FS-FAP, or range of channels used, MS-FAP. The FAP can be modelled in graph theoretic terms. This paper uses a 'permutation-based' steady-state GA adapted to solve both types of FAP. Problem decomposition techniques have been introduced to improve the GA performance. A large problem is decomposed into smaller subproblems consisting of distinct induced subgraphs. Then the GA is applied to each of them in turn producing partial solutions. Finally, these are recombined into a final solution of the original problem. We have used a basic decomposition based on the generalised-degree of the graph vertices and one obtained by solving the balanced graph partitioning problem. The GA produces better or comparable results with other widely known meta-heuristics in all the test problems considered.