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The hybrid genetic algorithm for solving nonlinear programming

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2 Author(s)
Wang Honggang ; Div. of Syst. Simulation & Comput. Application, Taiyuan Heavy Machinery Inst., China ; Zeng Jianchao

Genetic algorithms have been shown to be robust optimization algorithms for real value functions defined over domains of the form R n (R denotes the real number). But there exist some obstacles in genetic algorithms such as premature convergence and slow convergence speed. A new approach called Hybrid Genetic Algorithms (HGA) is presented to overcome these obstacles for nonlinear programming by combining genetic algorithms with the feasible path method after introducing a learning operator. Finally, the validity of the approach is illustrated by providing HGA for nonlinear programming

Published in:

Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on  (Volume:1 )

Date of Conference:

28-31 Oct 1997