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When applying genetic algorithms in a stochastic setting, each solution must be simulated a large number of times. Hence, the computing budget allocation can make a significant difference to the performance of the genetic algorithm. An easily implementable closed-form computing budget allocation rule of ranking the best m solutions out of total k solutions is proposed. The proposed budget allocation rule can perform better than the existing asymptotically optimal allocation rule for ranking the best m solutions. By integrating the proposed budget allocation rule, the search efficiency of genetic algorithms has significantly improved, as shown in the numerical examples. <\/jats:p>","DOI":"10.1177\/0037549714548095","type":"journal-article","created":{"date-parts":[[2014,9,13]],"date-time":"2014-09-13T04:49:40Z","timestamp":1410583780000},"page":"1146-1157","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":18,"title":["Simulation optimization using genetic algorithms with optimal computing budget allocation"],"prefix":"10.1177","volume":"90","author":[{"given":"Hui","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Statistics, Southwestern University of Finance and Economics, Chengdu, China"}]},{"given":"Loo Hay","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Industrial and Systems Engineering, National University of Singapore, 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