{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T08:53:20Z","timestamp":1709628800709},"reference-count":36,"publisher":"MIT Press","issue":"1","content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Model management is an essential component in data-driven surrogate-assisted evolutionary optimization. In model management, the solutions with a large degree of uncertainty in approximation play an important role. They can strengthen the exploration ability of algorithms and improve the accuracy of surrogates. However, there is no theoretical method to measure the uncertainty of prediction of Non-Gaussian process surrogates. To address this issue, this article proposes a method to measure the uncertainty. In this method, a stationary random field with a known zero mean is used to measure the uncertainty of prediction of Non-Gaussian process surrogates. Based on experimental analyses, this method is able to measure the uncertainty of prediction of Non-Gaussian process surrogates. The method's effectiveness is demonstrated on a set of benchmark problems in single surrogate and ensemble surrogates cases.<\/jats:p>","DOI":"10.1162\/evco_a_00316","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T17:43:29Z","timestamp":1664473409000},"page":"53-71","update-policy":"http:\/\/dx.doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":3,"title":["An Uncertainty Measure for Prediction of Non-Gaussian Process Surrogates"],"prefix":"10.1162","volume":"31","author":[{"given":"Caie","family":"Hu","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China caie_hu@cug.edu.cn"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanyou","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China sanyouzeng@gmail.com"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changhe","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences, Wuhan 430074, China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, 430074, China changhe.lw@gmail.com"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","published-online":{"date-parts":[[2023,3,1]]},"reference":[{"issue":"1","key":"2023030103554532600_","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s00500-003-0329-4","article-title":"Faster convergence by means of fitness estimation","volume":"9","author":"Branke","year":"2005","journal-title":"Soft 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