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Essentially, the two-way association balances the influence of two layers on the encoded individual by relaxing the control of the low-level layer and enhancing the control of the high-level layer, thus reaching the balance between the optimizations of zero and non-zero variables. Moreover, we propose a new evolutionary algorithm equipped with the modules and compare it with several state-of-the-art algorithms on 32 benchmark problems. Extensive experiments verify its effectiveness, as the proposed modules can improve the two-layer encoding and help the algorithm achieve superior performance on sparse LSMOPs.<\/jats:p>","DOI":"10.1007\/s40747-024-01489-x","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T20:07:12Z","timestamp":1717790832000},"page":"6319-6337","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Improving two-layer encoding of evolutionary algorithms for sparse large-scale multiobjective optimization problems"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6715-2875","authenticated-orcid":false,"given":"Jing","family":"Jiang","sequence":"first","affiliation":[]},{"given":"Huoyuan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Juanjuan","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Zhe","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Han","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,7]]},"reference":[{"issue":"1","key":"1489_CR1","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1007\/s00500-020-05149-3","volume":"25","author":"A Maghawry","year":"2021","unstructured":"Maghawry A, Hodhod R, Omar Y, Kholief M (2021) An approach for optimizing multi-objective problems using hybrid genetic algorithms. 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