Investigating Smart Sampling as a population initialization method for Differential Evolution in continuous problems

Recently, researches have shown that the performance of metaheuristics can be affected by population initialization. Opposition-based Differential Evolution (ODE), Quasi-Oppositional Differential Evolution (QODE), and Uniform-Quasi-Opposition Differential Evolution (UQODE) are three state-of-the-art methods that improve the performance of the Differential Evolution algorithm based on population initialization and different search strategies. In a different approach to achieve

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Using Smart Sampling to Discover Promising Regions and Increase the Efficiency of Differential Evolution

This paper presents a novel method to discover promising regions in a continuous search space. Using machine learning techniques, the algorithm named smart sampling was tested in hard known benchmark functions, and was able to find promising regions with solutions very close to the global optimum, significantly decreasing the number of evaluations needed by a

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