A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Meta-Modeling in Multiobjective Optimization
Multiobjective Optimization
Design of a motorcycle frame using neuroacceleration strategies in MOEAs
Journal of Heuristics
Application notes: MEBRA: multiobjective evolutionary-based risk assessment
IEEE Computational Intelligence Magazine
An evolutionary algorithm with spatially distributed surrogates for multiobjective optimization
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
Computers and Industrial Engineering
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In this paper, we propose a novel method for solving multiobjective optimization problems using reduced models. Our method, called objective exchange genetic algorithm for design optimization (OEGADO), is intended for solving real-world application problems. For such problems, the number of objective evaluations performed is a critical factor as a single objective evaluation can be quite expensive. The aim of our research is to reduce the number of objective evaluations needed to find a well-distributed sampling of the Pareto-optimal region by applying reduced models to steady-state multiobjective GAs. OEGADO runs several GAs concurrently with each GA optimizing one objective and forming a reduced model of its objective. At regular intervals, each GA exchanges its reduced model with the others. The GAs use these reduced models to bias their search toward compromise solutions. Empirical results in several engineering and benchmark domains comparing OEGADO with two state-of-the-art multiobjective evolutionary algorithms show that OEGADO outperformed them for difficult problems.