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An improved genetic algorithm for optimizing resource allocation using knowledge evolution and natural evolution

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5 Author(s)
Tang Ping ; Guangdong Univ. of Technol., Guangzhou, China ; Gao Changqing ; Tang Cheng ; Lee Gordon
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Decreasing the resource cost in industrial processes, especially in complex situations, is an important problem, particularly given our economic crisis. Efficient algorithms play an important role in reducing cost; in this paper, a resource allocation model is developed and an improved genetic algorithm (GA) is proposed that combines natural evolution with knowledge evolution, which can prevent the limited processing of natural evolution approaches. Simulation results are presented to illustrate that the proposed algorithm has the potential to perform better than classical methods in many different applications.

Published in:

World Automation Congress (WAC), 2010

Date of Conference:

19-23 Sept. 2010

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