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Mathematical Problems in Engineering
Volume 2011, Article ID 872415, 17 pages
Research Article

Statistical Design of Genetic Algorithms for Combinatorial Optimization Problems

1Faculty of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, P.O. Box 34185-1416, Iran
2Faculty of Industrial Engineering, K.N. Toosi University of Technology, Tehran 1999143344, Iran
3Department of Industrial Engineering, Sharif University of Technology, P.O. Box 11155-9414, Azadi Avenue, Tehran 1458889694, Iran

Received 21 May 2011; Accepted 7 July 2011

Academic Editor: J. J. Judice

Copyright © 2011 Moslem Shahsavar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Many genetic algorithms (GA) have been applied to solve different NP-complete combinatorial optimization problems so far. The striking point of using GA refers to selecting a combination of appropriate patterns in crossover, mutation, and and so forth and fine tuning of some parameters such as crossover probability, mutation probability, and and so forth. One way to design a robust GA is to select an optimal pattern and then to search for its parameter values using a tuning procedure. This paper addresses a methodology to both optimal pattern selection and the tuning phases by taking advantage of design of experiments and response surface methodology. To show the performances of the proposed procedure and demonstrate its applications, it is employed to design a robust GA to solve a project scheduling problem. Through the statistical comparison analyses between the performances of the proposed method and an existing GA, the effectiveness of the methodology is shown.