Real-coded genetic algorithm for machining condition optimization

  • Sung Soo Kim
  • Il-Hwan Kim
  • V. Mani
  • Hyung Jun Kim


In this paper, we consider the machining condition optimization models presented in earlier studies. Finding the optimal combination of machining conditions within the constraints is a difficult task. Hence, in earlier studies standard optimization methods are used. The non-linear nature of the objective function, and the constraints that need to be satisfied makes it difficult to use the standard optimization methods for the solution. In this paper, we present a real coded genetic algorithm (RCGA), to find the optimal combination of machining conditions. We present various issues related to real coded genetic algorithm such as solution representation, crossover operators, and repair algorithm in detail. We also present the results obtained for these models using real coded genetic algorithm and discuss the advantages of using real coded genetic algorithm for these problems. From the results obtained, we conclude that real coded genetic algorithm is reliable and accurate for solving the machining condition optimization models.


Machining conditions Multi-pass turning Optimization Real-coded genetic algorithm Single-pass turning 


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Copyright information

© Springer-Verlag London Limited 2007

Authors and Affiliations

  • Sung Soo Kim
    • 1
  • Il-Hwan Kim
    • 2
  • V. Mani
    • 3
  • Hyung Jun Kim
    • 1
  1. 1.Department of Industrial EngineeringKangwon National UniversityChunchonSouth Korea
  2. 2.Department of Electronic and Tele Communication EngineeringKangwon National UniversityChunchonSouth Korea
  3. 3.Department of Aerospace EngineeringIndian Institute of ScienceBangaloreIndia

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