A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms. (12th June 2016)

• Record Type: Journal Article
• Authors: Díaz-Manríquez, Alan; Toscano, Gregorio; Barron-Zambrano, Jose Hugo; Tello-Leal, Edgar
• Other Names: Graña Manuel Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2016(2016)
• Publication Date: 2016-06-12
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2016/9420460
• Languages: English
• ISSNs: 1687-5265

Abstract

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.