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Engineering Optimization Volume 48, 2016 - Issue 5
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Original Articles

Using support vector machine and dynamic parameter encoding to enhance global optimization

Z. ZhengCollege of Electronics and Information Engineering, Sichuan University, Chengdu, PR China
,
X. ChenCollege of Electronics and Information Engineering, Sichuan University, Chengdu, PR ChinaCorrespondence[email protected]
,
C. LiuCollege of Electronics and Information Engineering, Sichuan University, Chengdu, PR China
&
K. HuangCollege of Electronics and Information Engineering, Sichuan University, Chengdu, PR China
Pages 851-867 | Received 01 Feb 2014, Accepted 28 May 2015, Published online: 07 Jul 2015

Citations (5)

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Read on this site (3)

Fuqing Zhao, Guoqiang Yang, Yi Zhang, Wenchang Lei, Weimin Ma & Chuck Zhang. (2019) A novel hybrid combination optimization algorithm based on search area segmentation and fast Fourier transform. Engineering Optimization 51:5, pages 846-861.
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Shadi Abpeykar & Mehdi Ghatee. (2018) Decent direction methods on the feasible region recognized by supervised learning metamodels to solve unstructured problems. Journal of Information and Optimization Sciences 39:6, pages 1245-1262.
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Jin Cao, Zhibin Jiang & Kangzhou Wang. (2017) Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm. Engineering Optimization 49:7, pages 1197-1210.
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Articles from other publishers (2)

Bin Bai, Ze Li, Junyi Zhang & Wei Zhang. (2020) Application of Support Vector Machine-Based Classification Extremum Method in Flexible Mechanism. Journal of Mechanisms and Robotics 12:4.
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Peng Guo, Wenming Cheng & Yi Wang. (2017) Hybrid evolutionary algorithm with extreme machine learning fitness function evaluation for two-stage capacitated facility location problems. Expert Systems with Applications 71, pages 57-68.
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