A Novel Genetic Algorithm with Orthogonal Prediction for Global Numerical Optimization

  • Jun Zhang
  • Jing-Hui Zhong
  • Xiao-Min Hu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)


This paper proposes a novel orthogonal predictive local search (OPLS) to enhance the performance of the conventional genetic algorithms. OPLS operation predicts the most promising direction for the individuals to explore their neighborhood. It uses the orthogonal design method to sample orthogonal combinations to make the prediction. The resulting algorithm is termed the orthogonal predictive genetic algorithm (OPGA). OPGA has been tested on eleven numerical optimization functions in comparison with some typical algorithms. The results demonstrate the effectiveness of the proposed algorithm for achieving better solutions with a faster convergence speed.


Genetic algorithm orthogonal design method local search evolutionary algorithm numerical optimization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Holland, J.H.: Adaptation in Natural and Artificial Systems, 2nd edn. MIT Press, Cambridge (1992)Google Scholar
  2. 2.
    Wang, W.Y., Li, Y.H.: Evolutionary Learning of BMF Fuzzy-neural Networks Using a Reduced-form Genetic Algorithm. IEEE Trans. Syst. Man Cybern. Part B Cybern. 33, 966–976 (2003)CrossRefGoogle Scholar
  3. 3.
    Miller, J.A., Potter, W.D., Gandham, R.V., Lapena, C.N.: An Evaluation of Local Improvement Operators for Genetic Algorithms. IEEE Trans. Syst. Man Cybern. 23, 1340–1351 (1993)CrossRefGoogle Scholar
  4. 4.
    Ishibuchi, H., Yoshida, T., Murata, T.: Balance Between Genetic Search and Local Search in Memetic Algorithms for Multiobjective Permutation Flowshop Scheduling. IEEE Trans. Evol. Comput. 7, 204–223 (2003)CrossRefGoogle Scholar
  5. 5.
    Burke, E.K., Smith, A.J.: Hybrid Evolutionary Techniques for the Maintenance Scheduling Problem. IEEE Trans. Power Syst. 15, 122–128 (2000)CrossRefGoogle Scholar
  6. 6.
    Denqiz, B., Altiparmak, F., Smith, A.E.: Local Search Genetic Algorithm for Optimal Design of Reliable Networks. IEEE Trans. Evol. Comput. 1, 179–188 (1997)CrossRefGoogle Scholar
  7. 7.
    Baraglia, R., Hidalgo, J.I., Perego, R.: A Hybrid Heuristic for the Traveling Salesman Problem. IEEE Trans. Evol. Comput. 5, 613–622 (2001)CrossRefzbMATHGoogle Scholar
  8. 8.
    Tu, Z.G., Lu, Y.: A Robust Stochastic Genetic Algorithm (StGA) for Global Numerical Optimization. IEEE Trans. Evol. Comput. 8, 456–470 (2004)CrossRefGoogle Scholar
  9. 9.
    Yao, X., Liu, Y.: Evolutionary Programming Made Faster. IEEE Trans. Evol. Comput. 3, 82–102 (1999)CrossRefGoogle Scholar
  10. 10.
    Hedayat, A.S., Sloane, N.J.A., Stufken, J.: Orthogonal Arrays: Theory and Applications. Springer, New York (1999)CrossRefzbMATHGoogle Scholar
  11. 11.
    Montgomery, D.C.: Design and Analysis of Experiments, 5th edn. Wiley, New York (2000)Google Scholar
  12. 12.
    Hu, X.M., Zhang, J., Zhong, J.H.: An Enhanced Genetic Algorithm with Orthogonal Design. In: 2006 IEEE Congress on Evolutionary Computation, pp. 3174–3181. IEEE Press, New York (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jun Zhang
    • 1
  • Jing-Hui Zhong
    • 1
  • Xiao-Min Hu
    • 1
  1. 1.Department of Computer ScienceSUN Yat-sen UniversityGuangzhouChina

Personalised recommendations