Performance of Different Techniques Applied in Genetic Algorithm towards Benchmark Functions

  • Seng Poh Lim
  • Habibollah Haron
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7802)


Optimisation is the most interesting problems to be tested by using Artificial Intelligence (AI) methods because different optimal results will be obtained when different methods are implemented. Yet, there is no exact solution from the methods implemented because random function is usually applied. Genetic algorithm is a popular method which is used to solve the optimisation problems. However, no any methods can execute perfectly because the way of the method performs is different. Therefore, this paper proposed to compare the performance of GA with different operation techniques by using the benchmark functions. This can prove that different techniques applied in the operations can let GA produces different result. Based on the experiment result, GA is proved to perform well in the optimisation problems but it highly depends on the techniques implemented. The techniques for each operation have shown different performance in obtaining the time, minimum and average values for benchmark functions.


Genetic Algorithm Optimisation Benchmark Functions Performance 


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  1. 1.
    Sivanandam, S.N., Deepa, S.N.: Introduction to Genetic Algorithm. Springer (2008)Google Scholar
  2. 2.
    Lim, S.P., Haron, H.: Surface Reconstruction Techniques: A Review. In: Artif. Intell. Rev., pp. 1–20. Springer (2012), doi:10.1007/s10462-012-9329-zGoogle Scholar
  3. 3.
    Vavak, F., Fogarty, T.C.: A Comparative Study of Steady State and Generational Genetic Algorithms for Use in Nonstationary Environments. In: Fogarty, T.C. (ed.) AISB-WS 1996. LNCS, vol. 1143, pp. 297–304. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  4. 4.
    Engelbrecht, A.P.: Computational Intelligence. John Wiley & Sons, Ltd. (2002)Google Scholar
  5. 5.
    Rajasekaran, S., Pai, G.A.V.: Neural Networks, Fuzzy Logic, and Genetic Algorithms Systhesis and Applications. Prentice-Hall of India Private Limited (2007)Google Scholar
  6. 6.
    Mansour, N., Awad, M., El-Fakih, K.: Incremental Genetic Algorithm. The International Arab Journal of Information Technology 3(1), 42–47 (2006)Google Scholar
  7. 7.
    Lin, C.T., Cheng, W.C., Liang, S.F.: Neural–Network–Based Adaptive Hybrid–Reflectance Model for 3–D Surface Reconstruction. IEEE Transactions On Neural Networks 16(6), 1601–1615 (2005)CrossRefGoogle Scholar
  8. 8.
    Cheng, X., Wang, J., Wang, Q.: Leak–mending and Recruitment of Incomplete Points Data in 3D Reconstruction Based on Genetic Algorithm. In: Third International Conference on Natural Computation (ICNC 2007), pp. 259–263 (2007)Google Scholar
  9. 9.
    Saeedfar, A., Barkeshli, K.: Shape Reconstruction of Three–Dimensional Conducting Curved Plates Using Physical Optics, NURBS Modeling, and Genetic Algorithm. IEEE Transactions On Antennas And Propagation 54(9), 2497–2507 (2006)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Wang, S., Dhawan, A.P.: Shape–Based Reconstruction Of Skin Lesion For Multispectral Nevoscope Using Genetic Algorithm Optimization. In: 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2007, pp. 488–491 (2007)Google Scholar
  11. 11.
    Sivaraj, R.: A Review Of Selection Methods In Genetic Algorithm. International Journal of Engineering Science and Technology (IJEST) 3(5), 3792–3797 (2011)Google Scholar
  12. 12.
    Goldberg, D.F., Deb, K.: A Comparative Analysis of Selection Schemes Used in Genetic Algorithms, Foundations of Genetic Algorithms, pp. 69–93. Morgan Kaufmann Publisher (1991)Google Scholar
  13. 13.
    Chudasama, C., Shah, S.M., Panchal, M.: Comparison of Parents Selection Methods of Genetic Algorithm for TSP. In: International Conference on Computer Communication and Networks CSI-COMNET-2011, Proceedings, pp. 85–87. International Journal of Computer Applications, IJCA (2011)Google Scholar
  14. 14.
    Othman, Z., Subari, K., Morad, N.: Job Shop Scheduling with Alternative Machines Using Genetic Algorithm. Jurnal Teknologi, 41(D) Dis. 2004, 67–78 (2004)Google Scholar
  15. 15.
    Molga, M., Smutnicki, C.: Test function for optimization needs, 1–43 (2005),
  16. 16.
    Ortiz- Boyer, D., Hervás-Martínez, C., García-Pedrajas, N.: A Crossover Operator for Evolutionary Algorithms Based on Population Features. Journal of Artificial Intelligence Research 24, 1–48 (2005)MATHCrossRefGoogle Scholar
  17. 17.
    Hedar, A.R.: Test Functions for Unconstrained Global Optimization,

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Seng Poh Lim
    • 1
  • Habibollah Haron
    • 1
  1. 1.Department of Computer Science, Faculty of ComputingUniversiti Teknologi MalaysiaSkudaiMalaysia

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