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Continuous space pattern reduction for genetic clustering algorithm   
摘  要:   We have recently proposed a highly effective method for speeding up metaheuristics in solving combinatorial optimization problems called pattern reduction (PR). It is, however, limited to problems with solutions that are either binary or integer encoded. In this paper, we proposed a new pattern reduction algorithm named continuous space pattern reduction (CSPR) to overcome this limitation. Simulations show that the proposed algorithm can significantly reduce the computation time of k-means with genetic algorithm (KGA) for solving the data clustering problem using continuous encoding.
发  表:   Genetic and Evolutionary Computation Conference  2012

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