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Classical genetic algorithm suffers heavy pressure of fitness evaluation for time-consuming optimization problems. To address this problem, we present an efficient genetic algorithm by the combination with clustering methods. The high efficiency of the proposed method results from the fitness estimation and the schema discovery of partial individuals in current population and. Specifically, the clustering method used in this paper is affinity propagation. The numerical experiments demonstrate that the proposed method performs promisingly for well-known benchmark problems in the term of optimization accuracy.

Kuhn-Munkres is used inside assignment problem solver application. Instance generator application creates input file for the solver. Checker application verifies the solution computed by the solver. A bash script compiles, executes these apps.