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Optim."],"published-print":{"date-parts":[[2022,3,31]]},"abstract":"<jats:p>\n            Learning effective problem information from already explored search space in an optimization run, and utilizing it to improve the convergence of subsequent solutions, have represented important directions in Evolutionary Multi-objective Optimization (EMO) research. In this article, a machine learning (ML)-assisted approach is proposed that: (a)\n            <jats:italic>maps<\/jats:italic>\n            the solutions from earlier generations of an EMO run to the current non-dominated solutions\n            <jats:italic>in the decision space<\/jats:italic>\n            ; (b) learns the salient patterns in the\n            <jats:italic>mapping<\/jats:italic>\n            using an ML method, here an artificial neural network (ANN); and (c) uses the learned ML model to\n            <jats:italic>advance<\/jats:italic>\n            some of the subsequent offspring solutions in an adaptive manner. Such a multi-pronged approach, quite different from the popular\n            <jats:italic>surrogate-modeling<\/jats:italic>\n            methods, leads to what is here referred to as the\n            <jats:italic>Innovized Progress<\/jats:italic>\n            (IP) operator. On several test and engineering problems involving two and three objectives, with and without constraints, it is shown that an EMO algorithm assisted by the IP operator offers faster convergence behavior, compared to its base version independent of the IP operator. The results are encouraging, pave a new path for the performance improvement of EMO algorithms, and set the motivation for further exploration on more challenging problems.\n          <\/jats:p>","DOI":"10.1145\/3474059","type":"journal-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T17:12:26Z","timestamp":1636996346000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["A Learning-based\n            <i>Innovized<\/i>\n            Progress Operator for Faster Convergence in Evolutionary Multi-objective Optimization"],"prefix":"10.1145","volume":"2","author":[{"given":"Sukrit","family":"Mittal","sequence":"first","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Uttarakhand, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dhish Kumar","family":"Saxena","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Uttarakhand, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kalyanmoy","family":"Deb","sequence":"additional","affiliation":[{"name":"BEACON Center and Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erik D.","family":"Goodman","sequence":"additional","affiliation":[{"name":"BEACON Center and Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,11,15]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/0305215X.2010.528410"},{"key":"e_1_3_2_3_1","first-page":"513","volume-title":"Proceedings of the 7th International Conference on Evolutionary Multi-Criterion Optimization (EMO\u201913), LNCS 7811","author":"Bandaru S.","year":"2013","unstructured":"S. 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