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Zbl 1235.90130
Shahsavar, Moslem; Najafi, Amir Abbas; Niaki, Seyed Taghi Akhavan
Statistical design of genetic algorithms for combinatorial optimization problems. (English)
[J] Math. Probl. Eng. 2011, Article ID 872415, 17 p. (2011). ISSN 1024-123X; ISSN 1563-5147/e

Summary: 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.

MSC 2000:: *90C27 Combinatorial programming
90C59 Approximation methods and heuristics

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