Print Email Facebook Twitter On the impact of linkage learning, gene-pool optimal mixing, and non-redundant encoding on permutation optimization Title On the impact of linkage learning, gene-pool optimal mixing, and non-redundant encoding on permutation optimization Author Guijt, A. (TU Delft Algorithmics; Centrum Wiskunde & Informatica (CWI)) Luong, N.H. (TU Delft Algorithmics; Vietnam National University) Bosman, P.A.N. (TU Delft Algorithmics; Centrum Wiskunde & Informatica (CWI)) de Weerdt, M.M. (TU Delft Algorithmics) Date 2022 Abstract Gene-pool Optimal Mixing Evolutionary Algorithms (GOMEAs) have been shown to achieve state-of-the-art results on various types of optimization problems with various types of problem variables. Recently, a GOMEA for permutation spaces was introduced by leveraging the random keys encoding, obtaining promising first results on permutation flow shop instances. A key cited strength of GOMEAs is linkage learning, i.e., the ability to determine and leverage, during optimization, key dependencies between problem variables. However, the added value of linkage learning was not tested in depth for permutation GOMEA. Here, we introduce a new version of permutation GOMEA, called qGOMEA, that works directly in permutation space, removing the redundancy of using random keys. We additionally consider various linkage information sources, including random noise, in both GOMEA variants, and compare performance with various classic genetic algorithms on a wider range of problems than considered before. We find that, although the benefits of linkage learning are clearly visible for various artificial benchmark problems, this is far less the case for various real-world inspired problems. Finally, we find that qGOMEA performs best, and is more applicable to a wider range of permutation problems. Subject Estimation-of-distribution algorithmsGenetic algorithmsPermutation problems To reference this document use: http://resolver.tudelft.nl/uuid:4e4a617c-2057-4009-811b-c005e2f0c673 DOI https://doi.org/10.1016/j.swevo.2022.101044 ISSN 2210-6502 Source Swarm and Evolutionary Computation, 70 Part of collection Institutional Repository Document type journal article Rights © 2022 A. Guijt, N.H. Luong, P.A.N. Bosman, M.M. de Weerdt Files PDF 1_s2.0_S2210650222000165_main.pdf 886.84 KB Close viewer /islandora/object/uuid:4e4a617c-2057-4009-811b-c005e2f0c673/datastream/OBJ/view