e recently introduced Multi-Objective Gene-pool Optimal Mixing Evolutionary Algorithm (MO-GOMEA) exhibits excellent scalability in solving a wide range of challenging discrete multi-objective optimization problems. In this paper, we address scalability issues in solving multi-objective optimization problems with continuous variables by introducing the Multi-Objective Real-Valued GOMEA (MO-RV-GOMEA), which combines MO-GOMEA with aspects of the multi-objective estimation-of-distribution algorithm known as MAMaLGaM. MO-RV-GOMEA exploits linkage structure in optimization problems by performing distribution estimation, adaptation, and sampling as well as solution mixing based on an explicitlyde ned linkage model. Such a linkage model can be de ned a priori when some problem-speci c knowledge is available, or it can be learned from the population. e scalability of MO-RV-GOMEA using di erent linkage models is compared to the state-of-the-art multi-objective evolutionary algorithms NSGA-II and MAMaLGaM on a wide range of benchmark problems. MO-RV-GOMEA is found to retain the excellent scalability of MO-GOMEA through the successful exploitation of linkage structure, scaling substantially be er than NSGA-II and MAMaLGaM. is scalability is even further improved when partial evaluations are possible, achieving strongly sub-linear scalability in terms of the number of evaluations.
The recently-introduced Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) REF has been shown to be a promising optimizer especially in the case of graybox optimization where certain assumptions are made about types of problem-specific knowledge that can be exploited, which may well be the case in many real-world problems.
The multi-objective real-valued gene-pool optimal mixing evolutionary algorithm
GECCO '17
Computer Science