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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 metaheuristic to finally find this global...
The purpose of this paper is to propose new clustering technique on manifolds. This is achieved mainly with the help of tangent spaces that are determined by manifold learning. We embed a new searching algorithm based on differential evolution (DE). We present a simple convergence analysis with a design of experimental framework.
In order to comprehend the advantages and short-comings of each model-building algorithm they should be tested under similar conditions and isolated from the MOEDA it takes part of. In this work we will assess some of the main machine learning algorithms used or suitable for model-building in a controlled environment and under equal conditions. They are analyzed in terms of solution accuracy and computational...
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