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Statistical Design of Genetic Algorithms for Combinatorial Optimization Problems

机译：组合优化问题的遗传算法统计设计

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摘要

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.
机译：迄今为止，许多遗传算法（GA）已用于解决不同的NP完全组合优化问题。使用GA的显着点是指在交叉，突变等方面选择适当模式的组合，并对某些参数（如交叉概率，突变概率等）进行微调。设计鲁棒GA的一种方法是选择最佳模式，然后使用调整程序搜索其参数值。本文通过利用实验设计和响应面方法论，探讨了用于最佳模式选择和调整阶段的方法论。为了展示所提出程序的性能并演示其应用，它被用来设计一个鲁棒的遗传算法来解决项目调度问题。通过对提出的方法和现有遗传算法的性能进行统计比较分析，表明了该方法的有效性。

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  • 来源
    《Mathematical Problems in Engineering》 |2011年第3期|p.1-17|共17页
  • 作者

    Moslem Shahsavar; Amir Abbas Najafi; Seyed Taghi Akhavan Niaki;

  • 作者单位

    Faculty of Industrial and Mechanical Engineering, Islamic Azad University, Qazvin Branch, Qazvin, P.O. Box 34185-1416, Iran;

    Faculty of Industrial Engineering, K.N. Toosi University of Technology, Tehran 1999143344, Iran;

    Department of Industrial Engineering, Sharif University of Technology, P.O. Box 11155-9414, Azadi Avenue, Tehran 1458889694, Iran;

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