Abstract
Hypervolume optimization and objective-function approximators have been receiving considerable highlight in multi-objective optimization (MOO), as they provide increase in performance both in reducing the number of objective evaluations and in quality of the obtained solution. This thesis aims to contribute in these two fronts, proposing hybrid solutions for hypervolume maximization, whic…