Water Science and Engineering 2013, 6(4) 392-401 DOI:   10.3882/j.issn.1674-2370.2013.04.003  ISSN: 1674-2370 CN: 32-1785/TV

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genetic algorithm
 pollution sources
Quan-min BU
Zhan-jun WANG
Article by Quan-min BU
Article by Zhan-jun WANG
Article by Xing TONG

An improved genetic algorithm for searching for  pollution sources

Quan-min BU*1, 2, Zhan-jun WANG3, Xing TONG1, 2

1. School of Government, Nanjing University, Nanjing 210023, P. R. China
2. Center for Social Risk and Public Crisis Management of Nanjing University, Nanjing 210023, P. R. China
3. Institute of Chemical Industry of Forest Products, Nanjing 210042, P. R. China


 As an optimization method that has experienced rapid development over the past 20 years, the genetic algorithm has been successfully applied in many fields, but it requires repeated searches based on the characteristics of high-speed computer calculation and conditions of the known relationship between the objective function and independent variables. There are several hundred generations of evolvement, but the functional relationship is unknown in pollution source searches. Therefore, the genetic algorithm cannot be used directly. Certain improvements need to be made based on the actual situation, so that the genetic algorithm can adapt to the actual conditions of environmental problems, and can be used in environmental monitoring and environmental quality assessment. Therefore, a series of methods are proposed for the improvement of the genetic algorithm: (1) the initial generation of individual groups should be artificially set and move from lightly polluted areas to heavily polluted areas; (2) intervention measures should be introduced in the competition between individuals; (3) guide individuals should be added; and (4) specific improvement programs should be put forward. Finally, the scientific rigor and rationality of the improved genetic algorithm are proven through an example.   

Keywords genetic algorithm    fitness    selection    crossover    mutation    pollution sources  
Received 2012-09-06 Revised 2013-01-16 Online: 2013-10-30 
DOI: 10.3882/j.issn.1674-2370.2013.04.003

This work was supported by the Science and Technology Support Program of Jiangsu Province (Grant No. BE2010738), Jiangsu Colleges and Universities Natural Science Foundation Funded Project (Grant No. 08KJB620001), and the Qing Lan Project of Jiangsu Province.


Corresponding Authors: Quan-min BU
Email: qmbu@sina.com
About author:

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