Novelty in the Generation of Initial Population for Genetic Algorithms

  • Ali Karci
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3214)

Abstract

This paper presents a method of generating the initial population of genetic algorithms (GAs) for continuous global optimization by using upper and lower bounds of variables instead of a pseudo-random sequence. In order to make population lead to a more reliable solution, the generated initial population is much more evenly distributed, which can avoid causing rapid clustering around an arbitrary local optimal. Another important point is that the simplicity of a population illustrates the more symmetry, self-similarity, repetitions, periodicity such that they guide the computational process to go ahead to desired aim. We design a GA based on this initial population for global numerical optimization with continuous variables. So, the obtained population is more evenly distributed and resulting GA process is more robust. We executed the proposed algorithm to solve 3 benchmark problems with 128 dimensions and very large number of local minimums. The results showed that the proposed algorithm can find optimal or near-to-optimal solutions.

Keywords

Genetic Algorithms Initial Population Optimization 
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References

  1. 1.
    Goldberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  2. 2.
    Stybinski, M.A., Tang, T.S.: Experiments in Nonconvex Optimization: Stochastic Approximmation and Function Smoothing and Simulated Annealing. Neural Networks 3, 467–483 (1990)CrossRefGoogle Scholar
  3. 3.
    Hager, W.W., Hearn, W., Pardalos, P.M.: Large Scale of Optimization: State of the Art. Kluwer, Dordrecht (1994)MATHGoogle Scholar
  4. 4.
    Floudas, C.A., Pardalos, P.M.: State of the Art in Global Optimization: Computational Methods and Applications. Kluwer, Dordrecht (1996)MATHGoogle Scholar
  5. 5.
    Michalewicz, Z.: Genetic Algorithms+Data Structures=Evolution Programs. Springer, Heidelberg (1996)MATHGoogle Scholar
  6. 6.
    Siarry, P., Berthiau, G., Durbin, F., Haussy, J.: Enhanced Simulated Annealing for Global Minimizing Functions of Many-Continuous Variables. ACM Trans. Math. Software 23, 209–228 (1997)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Karcı, A., Çınar, A., Ergen, B.: Genetik Algoritma Kullanılarak Minimum Düğüm Kapsama Probleminin Çözümü, Elektrik-Elektronik-Bilgisayar Mühendisliği 8. Ulusal Kongresi, 6-12 Eylül, Gaziantep, Türkiye, pp. 20-23 (1999)Google Scholar
  8. 8.
    Karcı, A., Arslan, A.: Bidirectional Evolutionary Heuristic for the Minimum Vertex-Cover Problem. Journal of Computer and Electrical Engineering 29, 111–120 (2003)MATHCrossRefGoogle Scholar
  9. 9.
    Karcı, A., Çınar, A.: Comparison of Uniform Distributed Initial Population Method and Random Initial Population Method in Genetic Search. In: 15th International Symposium on Computer and Information Sciences, Istanbul, Turkey, October 11-13, 2000, pp. 159–166 (2000)Google Scholar
  10. 10.
    Karcı, A., Arslan, A.: Genetik Algoritmalarda Düzenli Populasyon, GAP IV: Mühendislik konferansı, 06-08 Haziran 2002, Harran Üniversitesi, Şanlıurfa (2002)Google Scholar
  11. 11.
    Karcı, A., Arslan, A.: Uniform Population in Genetic Algorithms. Journal of Electrical and Electronics, İstanbul Ünv. Temmuz (2002)Google Scholar
  12. 12.
    Karci, A.: Genetik Algoritmalarda Iraksama ve Yerel Çözümde Kalma Problemlerinin Giderilmesi, F. Ü. Fen Bilimleri Enst. (2002)Google Scholar
  13. 13.
    Yao, X., Liu, Y.: Fast Evolution Strategies. In: Evolutionary Programming VI, pp. 151–161. Springer, Heidelberg (1997)Google Scholar
  14. 14.
    Siarry, P., Berthiau, G., Durbin, F., Haussy, J.: Enhanced Simulated Annealing for Globally Minimizing Functions of Many-Continuous Variables. ACM Trans. Math. Software 23, 209–228 (1997)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Angeline, P.J.: Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences. In: Proc. Evol. Prog. VII, pp. 601–610. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  16. 16.
    Chellapilla, K.: Combining Mutation Operators in Evolutionary Programming. IEEE Trans. Evol. Comput. 2, 91–96 (1998)CrossRefGoogle Scholar
  17. 17.
    Leung, Y.-W., Wang, Y.: An Orthogonal Genetic Algorithm with Quantization for Global Numerical Optimization. In: Evolutionary Computation, vol. 5, pp. 41–53. MIT Press, Cambridge (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Ali Karci
    • 1
  1. 1.Department of Computer EngineeringFirat UniversityElaziğTurkey

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