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  2006, Vol. 19 Issue (6): 794-800    DOI:
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A Mixed Strategies Pareto Evolutionary Programming
DONG HongBin1,2, HUANG HouKuan1, HE Jun1, HOU Wei3 , MU ChengPo1
1.School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044
2.Department of Computer Science, Harbin Normal University, Harbin 150080
3.Department of Computer Science, Agricultural University of the Northeast, Harbin 150030

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Abstract  A evolutionary approach to solve the multiobjective optimization problems, Mixed Strategies Pareto Evolutionary Programming (MSPEP), is presented. Based on the performance of mutation strategies, the mixed strategy distribution is dynamically adjusted. By combining the Pareto strength ranking procedure with the mixed mutation strategies, a new evolutionary algorithm is proposed. The proposed approach is compared with other evolutionary optimization techniques in several benchmark functions. Experimental results demonstrate that the proposed method could rapidly converge to the Pareto optimal front and spread widely along the front.
Key wordsMultiobjective Optimization      Pareto Optimal Front      Mixed Strategy      Evolutionary Programming     
Received: 02 November 2005     
ZTFLH: TP18  
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DONG HongBin
HUANG HouKuan
HE Jun
HOU Wei
MU ChengPo
Cite this article:   
DONG HongBin,HUANG HouKuan,HE Jun等. A Mixed Strategies Pareto Evolutionary Programming[J]. , 2006, 19(6): 794-800.
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http://manu46.magtech.com.cn/Jweb_prai/EN/      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2006/V19/I6/794
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