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This strategy involves two major components, i.e., constructing local Pareto fronts through exact methods and picking the best one via decomposition approaches. The empirical study shows F-MOEA\/D can obtain better approximations of the test instances against several alternative multiobjective evolutionary algorithms with a same time budget. Meanwhile, on two large instances with 7964 and 9090 assets, F-MOEA\/D still performs well given that a multiobjective mathematical method does not finish in 7\u00a0days.<\/jats:p>","DOI":"10.1007\/s40747-022-00715-8","type":"journal-article","created":{"date-parts":[[2022,3,31]],"date-time":"2022-03-31T07:02:46Z","timestamp":1648710166000},"page":"4301-4317","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Multiobjective portfolio optimization via Pareto front evolution"],"prefix":"10.1007","volume":"8","author":[{"given":"Yi","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4768-5946","authenticated-orcid":false,"given":"Aimin","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,31]]},"reference":[{"key":"715_CR1","first-page":"77","volume":"7","author":"H Markowitz","year":"1952","unstructured":"Markowitz H (1952) Portfolio selection. 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