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• W2772079570 abstract "Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008 and 2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approximation-based algorithms. We also compare these algorithms based on different criteria such as metamodeling technique and evolutionary algorithm used, type and dimensions of the problem solved, handling constraints, training time and the type of evolution control. Furthermore, we identify and discuss some promising elements and major issues among algorithms in the literature related to using an approximation and numerical settings used. In addition, we discuss selecting an algorithm to solve a given computationally expensive multiobjective optimization problem based on the dimensions in both objective and decision spaces and the computation budget available." @default.
• W2772079570 created "2017-12-22" @default.
• W2772079570 creator A5005908576 @default.
• W2772079570 creator A5020945863 @default.
• W2772079570 creator A5057408251 @default.
• W2772079570 creator A5075390340 @default.
• W2772079570 date "2017-12-11" @default.
• W2772079570 modified "2023-10-18" @default.
• W2772079570 title "A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms" @default.
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