title: A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms.
abstract: Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model. In this paper, we center our focus on classifying multiobjective evolutionary algorithms with respect to their integration with surrogate models. This interaction has led us to classify similar approaches and identify advantages and disadvantages of each class.
authors.affiliation: Facultad de Ingeniería y Ciencias, Universidad Autónoma de Tamaulipas, 87000 Victoria, TAMPS, Mexico.
authors.forename: Alan
authors.initials: A
authors.lastname: Díaz-Manríquez
authors.affiliation: Information Technology Laboratory, CINVESTAV-Tamaulipas, 87130 Victoria, TAMPS, Mexico.
authors.forename: Gregorio
authors.initials: G
authors.lastname: Toscano
authors.affiliation: Facultad de Ingeniería y Ciencias, Universidad Autónoma de Tamaulipas, 87000 Victoria, TAMPS, Mexico.
authors.forename: Jose Hugo
authors.initials: JH
authors.lastname: Barron-Zambrano
authors.affiliation: Facultad de Ingeniería y Ciencias, Universidad Autónoma de Tamaulipas, 87000 Victoria, TAMPS, Mexico.
authors.forename: Edgar
authors.initials: E
authors.lastname: Tello-Leal
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D003198:Computer Simulation; D006801:Humans; D008962:Models, Theoretical