{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:34:37Z","timestamp":1770748477453,"version":"3.50.0"},"reference-count":24,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2015,7,29]],"date-time":"2015-07-29T00:00:00Z","timestamp":1438128000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2015,7,29]]},"abstract":"<jats:p>Evolutionary Algorithm provides a framework that is largely applicable to particular problems including multiobjective optimization problems, basically for the ease of their implementation and their capability to perform efficient parallel search. Indeed, in some cases, expensive multiobjective optimization evaluations might be a challenge to restrict the number of explicit fitness evaluations in multiobjective evolutionary algorithms. Accordingly, this article presents a novel approach that tackles this problem so as to not only decrease the number of fitness evaluations but also to improve the performance. During evolution, our proposed approach selects fit individuals based on the knowledge acquired throughout the search, and performs explicit fitness evaluations on these individuals. A comprehensive comparative analysis of a wide range of well-established test problems, selected from both traditional and state-of-the-art benchmarks, has been presented. Afterward, the effectiveness of the obtained results is compared with some of the state-of-the-art methods using two well-known metrics- i.e. Hypervolume and Inverted Generational Distance (IGD). The experiments of our implemented approach is performed to illustrate that our proposal seems to be promising and would prove more efficient than other approaches in terms of both the performance and the computational cost.<\/jats:p>","DOI":"10.3233\/ifs-151687","type":"journal-article","created":{"date-parts":[[2015,11,10]],"date-time":"2015-11-10T11:37:15Z","timestamp":1447155435000},"page":"2111-2131","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["A fuzzy based approach for fitness approximation in multi-objective evolutionary algorithms"],"prefix":"10.1177","volume":"29","author":[{"given":"Zahra","family":"Pourbahman","sequence":"first","affiliation":[{"name":"Department of Electronic and Computer Engineering, Shiraz University, Shiraz, Iran"}]},{"given":"Ali","family":"Hamzeh","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, Shiraz University, Shiraz, Iran"}]}],"member":"179","published-online":{"date-parts":[[2015,8,13]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Deb K 2011 Multi-objective Optimization Using Evolutionary Algorithms: An Introduction Technical Report 2011003 Indian Institute of Technology Kanpur","DOI":"10.1007\/978-0-85729-652-8_1"},{"issue":"5","key":"e_1_3_1_3_2","first-page":"170","article-title":"Reducing the computational cost in multi-objective evolutionary algorithms by filtering worthless individuals","volume":"10","author":"Pourbahman Z","year":"2013","unstructured":"Pourbahman Z, Hamzeh A 2013 Reducing the computational cost in multi-objective evolutionary algorithms by filtering worthless individuals International Journal of ComputerScience Issues 10 5(2) 170 181","journal-title":"International Journal of ComputerScience Issues"},{"key":"e_1_3_1_4_2","first-page":"7","article-title":"Single-objective vs. Multi-objective Optimisation for Integrated Decision Support","volume":"1","author":"Savic D","year":"2002","unstructured":"Savic D 2002 Single-objective vs. Multi-objective Optimisation for Integrated Decision Support Proceedings of the First Biennial Meeting of the International Environmental Modelling and Software Society 1 7 12 Lugano, Switzerland","journal-title":"Proceedings of the First Biennial Meeting of the International Environmental Modelling and Software Society"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-10701-6_2"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2009.09.001"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-003-0328-5"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2011.05.001"},{"key":"e_1_3_1_9_2","first-page":"1","article-title":"Pareto Rank Learning in Multi-objective Evolutionary Algorithms","author":"Seah CW","year":"2012","unstructured":"Seah CW, Ong YS, Tsang IW, Jiang S 2012 Pareto Rank Learning in Multi-objective Evolutionary Algorithms In Proceedings of the IEEE Congress on Evolutionary Computation 1 8","journal-title":"In Proceedings of the IEEE Congress on Evolutionary Computation"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/1830483.1830571"},{"key":"e_1_3_1_12_2","first-page":"1979","article-title":"A Pareto-Compliant Surrogate Approach for Multi-objective Optimization","author":"Loshchilov I","year":"2010","unstructured":"Loshchilov I, Schoenauer M, Sebag M 2010 A Pareto-Compliant Surrogate Approach for Multi-objective Optimization Proceedings of the Twelfth Annual Conference on Genetic and Evolutionary Computation 1979 1982","journal-title":"Proceedings of the Twelfth Annual Conference on Genetic and Evolutionary Computation"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-17298-4_24"},{"key":"e_1_3_1_14_2","unstructured":"Davarynejad M 2007 Department Electrical Engineering-Control Program Adaptive Fuzzy Fitness Granulation in Evolutionary Algorithms for Complex Optimization Ferdowsi University of Mashhad Iran M.S. 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