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To effectively solve these multiple optimization tasks, an improved evolutionary algorithm with transfer migration is developed, which can enhance the optimization efficiency and robustness by useful information exchange between these similar optimization tasks. Second, a novel ensemble method is proposed to integrate the multiple sub-models into the final model. The significance of each basis function is scored according to the error estimation of the sub-models and the occurrence frequency of the basis functions in all the sub-models. Then the basis functions are ranked and selected based on the bias-corrected Akaike\u2019s information criterion. PRS-MOEM can effectively mitigate the negative influence from the sub-models with large prediction error, and alleviate the uncertain impact resulting from the randomness of training subsets. Thus the basis function selection accuracy and robustness can be enhanced. Seven numerical examples and an engineering problem are utilized to test and verify the effectiveness of PRS-MOEM.<\/jats:p>","DOI":"10.1007\/s40747-021-00568-7","type":"journal-article","created":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T16:04:53Z","timestamp":1636560293000},"page":"1015-1034","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Polynomial Response Surface based on basis function selection by multitask optimization and ensemble modeling"],"prefix":"10.1007","volume":"8","author":[{"given":"Yong","family":"Zhao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4254-1043","authenticated-orcid":false,"given":"Siyu","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Xianqi","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yufeng","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Xiaohu","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,10]]},"reference":[{"issue":"1\u20133","key":"568_CR1","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.paerosci.2008.11.001","volume":"45","author":"AI Forrester","year":"2009","unstructured":"Forrester AI, Keane AJ (2009) Recent advances in surrogate-based optimization. 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