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An optimization method of technological processes to complex products using knowledge-based genetic algorithm

Yuchun Yao (Economics School, Jilin University, Changchun, China)
Yan Wang (School of Humanities and Social Sciences, Beihang University, Beijing, China)
Lining Xing (Department of Management, College of Information System and Management, National University of Defense Technology, Changsha, China)
Hao Xu (College of Computer Science and Technology, Jilin University, Changchun, China)

Journal of Knowledge Management

ISSN: 1367-3270

Article publication date: 9 February 2015




This paper applies the knowledge-based genetic algorithm to solve the optimization problem in complex products technological processes.


The knowledge-based genetic algorithm (KGA) is defined as a hybrid genetic algorithm (GA) which combined the GA model with the knowledge model. The GA model searches the feasible space of optimization problem based on the “neighborhood search” mechanism. The knowledge model discovers some knowledge from the previous optimization process, and applies the obtained knowledge to guide the subsequent optimization process.


The experimental results suggest that the proposed KGA is feasible and available. The effective integration of GA model and knowledge model has greatly improved the optimization performance of KGA.


The technological innovation of complex products is one of effective approaches to establish the core competitiveness in future. For this reason, the KGA is proposed to the technological processes optimization of complex products.



Yao, Y., Wang, Y., Xing, L. and Xu, H. (2015), "An optimization method of technological processes to complex products using knowledge-based genetic algorithm", Journal of Knowledge Management, Vol. 19 No. 1, pp. 82-94.



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Copyright © 2015, Emerald Group Publishing Limited

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