Genovese, Federico (2017) Performance assessment of Surrogate model integrated with sensitivity analysis in multi-objective optimization. [Magistrali biennali]
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This Thesis develops a new multi-objective heuristic algorithm. The optimum searching task is performed by a standard genetic algorithm. Furthermore, it is assisted by the Response Surface Methodology surrogate model and by two sensitivity analysis methods: the Variance-based, also known as Sobol’ analysis, and the Elementary Effects. Once built the entire method, it is compared on several multi-objective problems with some other algorithms.
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