The multi-objective real-valued gene-pool optimal mixing evolutionary algorithm

Anton Bouter, Ngoc Hoang Luong, Cees Witteveen, Tanja Alderliesten, Peter Bosman

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

16 Citations (Scopus)


The 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 explicitly-defined linkage model. Such a linkage model can be defined a priori when some problem-specific knowledge is available, or it can be learned from the population. The scalability of MO-RV-GOMEA using different 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 better than NSGA-II and MAMaLGaM. This scalability is even further improved when partial evaluations are possible, achieving strongly sub-linear scalability in terms of the number of evaluations.
Original languageEnglish
Title of host publicationGECCO 2017 - Proceedings of the 2017 Genetic and Evolutionary Computation Conference
Number of pages8
Publication statusPublished - 1 Jul 2017
EventGECCO 2017: Genetic and Evolutionary Computation Conference - Berlin, Germany
Duration: 15 Jul 201719 Jul 2017


ConferenceGECCO 2017
OtherA Recombination of the 26th International Conference on Genetic Algorithms (ICGA) and the 22nd Annual Genetic Programming Conference (GP).
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