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• W2787343346 abstract "The Genetic Algorithm is a well-known heuristic-based optimization technique, but by using it in expensive problems, the excessive use of the fitness function can produce heavy computational loads. The purpose of this paper is to avoid this drawback by employing surrogate algorithms, providing approximate but sufficiently accurate solutions. Particularly the surrogate is based on granular computing and fuzzy logic. The difference from our related previous works consists into treat the algorithm's search, not as a black box, but extracting certain knowledge of granules' behavior represented by a new form of fuzzy aptitude functions. The primary objective is to manage the process intelligently, not only avoiding unnecessary usage of fitness evaluations but also improving the convergence with the extracted knowledge and the parameters' update with a newly built neural network structure. The algorithm shows satisfactory results in saving unnecessary evaluations and in time; in this case, we proved the optimization process related to parameters' estimation of a permanent magnet synchronous machine (PMSM) and some common benchmark functions used in GA assessments." @default.
• W2787343346 created "2018-02-23" @default.
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• W2787343346 date "2017-11-01" @default.
• W2787343346 modified "2023-10-18" @default.
• W2787343346 title "Surrogate modeling and knowledge extraction in ga applied to a parameter's estimation case" @default.
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• W2787343346 doi "https://doi.org/10.1109/ssci.2017.8280971" @default.
• W2787343346 hasPublicationYear "2017" @default.
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