Partitioning a graph with a parallel genetic algorithm
We present a parallel genetic algorithm for the k way graph partitioning problem. The algorithm uses selection in local neighborhood and sophisticated genetic operators. For a sample problem the algorithm has found better solutions than those found by recent GPP algorithms. The success of the parallel genetic algorithm depends on the representation, a suitable crossover operator and an efficient local hill climbing method which is used to restrict the solution space.
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