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Evolutionary algorithms (EAs) are well-known optimization approaches to deal with nonlinear and complex problems. However, these population-based algorithms are computationally expensive due to the slow nature of the evolutionary process. This paper proposes an improved differential evolution algorithm (CDE). On the one hand CDE combines the advantages of DE with the mechanisms of Pareto based ranking...
Control parameter settings influence the convergence probability and convergence speed of evolutionary algorithms but it is often not obvious how to choose them. In this work an adaptive approach for setting the control parameters of a multi-objective differential evolution algorithm is presented. The adaptive approach is based on methods from design of experiments, so it is able to detect significant...
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