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To provide an efficient multiobjective optimizer, an approximation technique based on the moving least squares approximation is integrated into an improved genetic algorithm. In order to use fully, both the a posteriori information gathered from the latest searched nondominated solutions and the a priori knowledge about the search space and individuals, in guiding the search towards more and better...
In order to use the information gathered from all non-dominated solutions of an optimizer and to guide the search toward more and better Pareto solutions, this paper proposes an efficient and robust vector optimal algorithm that integrates approximation techniques into an improved genetic algorithm. Numerical results are reported to validate the proposed work
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