A bivariate probabilistic model-building genetic algorithm for graph bipartitioning

We investigate a bi-variate probabilistic model-building GA for the graph bipartitioning problem.The graph bipartitioning problem is a grouping problem that requires some modi.cations to the standard construction of the dependency tree.We also increase the computational efficiency of the Bi-PMBGA by restricting the dependency tree to the edges of the graph to be partitioned.Experimental results indicate that the Bi-PMBGA performs signi .cantly better than the multi-start local search.Compared to a genetic local search algorithm the Bi-PMBGA performs slightly worse on some of the graphs considered here.

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