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Thus, a decision maker must be employed to provide preferences to find the most preferred solution. However, decision makers often lack support in providing preferences and insights in exploring the solutions available.<\/jats:p>\n          <jats:p>We explore the combination of learnable evolutionary models with interactive indicator-based evolutionary multiobjective optimization to create a learnable evolutionary multiobjective optimization method. Furthermore, we leverage interpretable machine learning to provide decision makers with potential insights about the problem being solved in the form of rule-based explanations. In fact, we show that a learnable evolutionary multiobjective optimization method can offer advantages in the search for solutions to a multiobjective optimization problem. We also provide an open source software framework for other researchers to implement and explore our ideas in their own works.<\/jats:p>\n          <jats:p>\n            Our work is a step toward establishing a new paradigm in the field on multiobjective optimization:\n            <jats:italic>explainable and learnable multiobjective optimization<\/jats:italic>\n            . We take the first steps toward this new research direction and provide other researchers and practitioners with necessary tools and ideas to further contribute to this field.\n          <\/jats:p>","DOI":"10.1145\/3626104","type":"journal-article","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T16:30:56Z","timestamp":1695918656000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Exploring the Explainable Aspects and Performance of a Learnable Evolutionary Multiobjective Optimization Method"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4673-7388","authenticated-orcid":false,"given":"Giovanni","family":"Misitano","sequence":"first","affiliation":[{"name":"University of Jyv\u00e4skyl\u00e4, Jyv\u00e4skyl\u00e4, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,2,23]]},"reference":[{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448301"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Bekir Afsar Johanna Silvennoinen and Kaisa Miettinen. 2023. A systematic way of structuring real-world multiobjective optimization problems. In Evolutionary Multi-Criterion Optimization. Lecture Notes in Computer Science Vol. 13970. Springer 593\u2013605.","DOI":"10.1007\/978-3-031-27250-9_42"},{"key":"e_1_2_1_4_1","first-page":"1","article-title":"Designing empirical experiments to compare interactive multiobjective optimization methods","author":"Afsar Bekir","year":"2022","unstructured":"Bekir Afsar, Johanna Silvennoinen, Giovanni Misitano, Francisco Ruiz, Ana B. Ruiz, and Kaisa Miettinen. 2022. Designing empirical experiments to compare interactive multiobjective optimization methods. 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