A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Immune Genetic Algorithm for Weapon-Target Assignment Problem
IITA '07 Proceedings of the Workshop on Intelligent Information Technology Application
An evolutionary algorithm for some cases of the single-source constrained plant location problem
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Exact and Heuristic Algorithms for the Weapon-Target Assignment Problem
Operations Research
Evolutionary codings and operators for the terminal assignment problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In the Weapon-Target Assignment Problem, m enemy targets are inbound, each with a value Vj representing the damage it may do. The defense has n weapons, and the probability that weapon i will kill target j is pij. The problem is to assign the weapons to targets so as to reduce as much as possible the total expected value of the targets. A greedy heuristic for this problem repeatedly assigns a weapon to a target to maximally degrade the target's value. Two genetic algorithms encode candidate assignments as strings of target labels indexed by weapon labels and as permutations of weapon labels decoded by a greedy algorithm, respectively. Both GAs can be seeded with the greedy heuristic's solution. In comparisons on fifteen randomly-generated problem instances, all the algorithms significantly reduced the hypothetical strikes' values, but the greedy heuristic was both effective and fast, while the seeded permutation-coded GA returned the best results. The times that all the GAs require grow quickly with problem size.