Terminal assignment in a communications network using genetic algorithms
CSC '94 Proceedings of the 22nd annual ACM computer science conference on Scaling up : meeting the challenge of complexity in real-world computing applications: meeting the challenge of complexity in real-world computing applications
Heuristic algorithms for the terminal assignment problem
SAC '97 Proceedings of the 1997 ACM symposium on Applied computing
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
A hybrid Hopfield network-genetic algorithm approach for the terminal assignment problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
String- and permutation-coded genetic algorithms for the static weapon-target assignment problem
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Swarm optimisation algorithms applied to large balanced communication networks
Journal of Network and Computer Applications
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Given a collection of terminals, each with a demand, a collection of concentrators, each with a capacity, and costs of connecting the terminals to the concentrators, the terminal assignment problem seeks a set of such connections of minimum cost and without the total demand at any concentrator exceeding its capacity. One genetic algorithm for this problem encodes candidate solutions as strings of concentrator labels; in three other GAs, chromosomes are permutations of terminal labels decoded by a greedy decoder. In comparisons on 40 instances of the problem, the string-coded GA consistently performs poorly, while among the three permutation-coded GAs, one that applies only mutation almost always outperforms the other two, which use crossover operators as well as mutation.