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Statistical design of genetic algorithms for combinatorial optimization problems

Shahsavar, M ; Sharif University of Technology | 2011

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  1. Type of Document: Article
  2. DOI: 10.1155/2011/872415
  3. Publisher: 2011
  4. Abstract:
  5. 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
  6. Keywords:
  7. Combinatorial optimization problems ; Cross-over probability ; Fine tuning ; Mutation probability ; NP Complete ; Parameter values ; Pattern selection ; Project scheduling problem ; Response Surface Methodology ; Statistical comparisons ; Statistical design ; Striking point ; Combinatorial optimization ; Design ; Design of experiments ; Optimization ; Genetic algorithms
  8. Source: Mathematical Problems in Engineering ; Volume 2011 , 2011 ; 1024123X (ISSN)
  9. URL: http://www.hindawi.com/journals/mpe/2011/872415