Optimization with constraints using a cultured differential evolution approach

In this paper we propose a cultural algorithm, where different knowledge sources modify the variation operator of a differential evolution algorithm. Differential evolution is used as a basis for the population, variation and selection processes. The experiments performed show that the cultured differential evolution is able to reduce the number of fitness function evaluations needed to obtain a good aproximation of the optimum value in constrained real-parameter optimization. Comparisons are provided with respect to three techniques that are representative of the state-of-the-art in the area.

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