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First, the individuals are evaluated using expensive fitness functions of the complex problems, and some dominant solutions are selected to construct the surrogate model. The restricted Boltzmann machine (RBM) is built and trained with the dominant solutions to implicitly extract the distributed representative information of the decision variables in the promising subset. The visible layer\u2019s probability of the RBM is designed as the sampling probability model of the estimation of distribution algorithm (EDA) and is updated dynamically along with the update of the dominant subsets. Second, according to the energy function of the RBM, a fitness surrogate is developed to approximate the expensive individual fitness evaluations and participates in the evolutionary process to reduce the computational cost. Finally, model management is developed to train and update the RBM model with newly dominant solutions. A comparison of the proposed algorithm with several state\u2010of\u2010the\u2010art surrogate\u2010assisted evolutionary algorithms demonstrates that the proposed algorithm effectively and efficiently solves complex optimization problems with smaller computational cost.<\/jats:p>","DOI":"10.1155\/2018\/2609014","type":"journal-article","created":{"date-parts":[[2018,11,1]],"date-time":"2018-11-01T23:49:29Z","timestamp":1541116169000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Restricted Boltzmann Machine\u2010Assisted Estimation of Distribution Algorithm for Complex Problems"],"prefix":"10.1155","volume":"2018","author":[{"given":"Lin","family":"Bao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1386-6853","authenticated-orcid":false,"given":"Xiaoyan","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Guangyi","family":"Man","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Shao","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2018,11]]},"reference":[{"volume-title":"Technical Report Series B, Scientific Computing No. B 4\/2015","year":"2015","author":"Chugh T.","key":"e_1_2_9_1_2"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2016.2638898"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2016.2633542"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEMC.2017.2760379"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2016.2614272"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2017.2688132"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13634-016-0309-3"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2016.2578341"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2017.2711038"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/tevc.2002.800884"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2009.2027359"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2011.05.001"},{"key":"e_1_2_9_13_2","doi-asserted-by":"crossref","unstructured":"SunX. 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