Skip to main content

Advertisement

SpringerLink
Log in

An Improved Genetic Algorithm with Average-bound Crossover and Wavelet Mutation Operations

  • Original Paper
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

This paper presents a real-coded genetic algorithm (RCGA) with new genetic operations (crossover and mutation). They are called the average-bound crossover and wavelet mutation. By introducing the proposed genetic operations, both the solution quality and stability are better than the RCGA with conventional genetic operations. A suite of benchmark test functions are used to evaluate the performance of the proposed algorithm. Application examples on economic load dispatch and tuning an associative-memory neural network are used to show the performance of the proposed RCGA.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Caponetto R, Fortuna L, Nunnari G, Occhipinti L, Xibilia MG (2000) Soft computing for greenhouse climate control. IEEE Trans Fuzzy Syst 8(6):753–760

    Article  Google Scholar 

  2. Chen PH, Chang HC (1995) Large-scale economic dispatch by genetic algorithm. IEEE Trans Power Syst 10:117–124

    Article  MathSciNet  Google Scholar 

  3. Daubechies I (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE Trans Inform Theor 36(5):961–1005

    Article  MathSciNet  MATH  Google Scholar 

  4. Daubechies I (1992) Ten lectures on wavelets. Society for Industrial and Applied Mathematics, Philadelphia, PA

    MATH  Google Scholar 

  5. Davis L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, Newyork

    Google Scholar 

  6. De Jong KA (1975) An analysis of the behavior of a class of genetic adaptive systems. PhD Thesis, University of Michigan, Ann Arbor, MI

    Google Scholar 

  7. Eshelman LJ, Schaffer JD (1993) Real-coded genetic algorithms and interval-schemata. Found Genet algorithms 2:187–202

    Google Scholar 

  8. Goldstein AA, Price IF (1971) On descent from local minima. Math Comput 25(115)

  9. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, MI

    Google Scholar 

  10. Joines J, Houck C (1994) On the use of non-stationary penalty functions to solve constrained optimisation problems with genetic algorithm. In: Proceedings of 1994 International Symposium evolutionary computation, Ordando, pp 579–584

  11. Juidette H, Youlal H (2000) Fuzzy dynamic path planning using genetic algorithms. Electr Lett 36(4):374–376

    Article  Google Scholar 

  12. Kita H, Ono I, Kobayashi S (1998) Theoretical analysis of the unimodal normal distribution crossover for real-coded genetic algorithms. In: Proceedings of the congress on evolutionary computational (CEC1998), world congress on computational intelligence (WCCI 1998), May 4–9, pp 529–534

  13. Lam HK, Leung FHF, Tam PKS (2003) Design and stability analysis of fuzzy model based nonlinear controller for nonlinear systems using genetic algorithm. IEEE Trans Syst Man Cybern B Cybern 33(2):250–257

    Article  Google Scholar 

  14. Leung FHF, Lam HK, Ling SH, Tam PKS (2003) Tuning of the structure and parameters of neural network using an improved genetic algorithm. IEEE Trans Neural Netw 14(1):79–88

    Article  Google Scholar 

  15. Leung FHF, Lam HK, Ling SH, Tam PKS (2004) Optimal and stable fuzzy controllers for nonlinear systems using an improved genetic algorithm. IEEE Trans Ind Electron 51(1):172–182

    Article  Google Scholar 

  16. Ling SH, Leung FHF, Lam HK, Tam PKS (2003) Short-term electric load forecasting based on a neural fuzzy network. IEEE Trans Industr Electron 50(6):1305–1316

    Article  Google Scholar 

  17. Liu BD, Chen CY, Tsao JY (2001) Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms. IEEE Trans Syst Man Cybern B 31(1):32–53

    Article  Google Scholar 

  18. Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693

    Article  MATH  Google Scholar 

  19. Michalewicz Z (1994) Genetic algorithm + data structures = evolution programs, 2nd edn. Springer Berlin Heidelberg, New York

    Google Scholar 

  20. Mühlenkein H, Schlierkamp-Voosen D (1993) Predictive models for the breeder genetic algorithm I. Continuous parameter optimisation. Evol Comput 1(1):25–49

    Google Scholar 

  21. Neubauer A (1997) A theoretical analysis of the non-uniform mutation operator for the modified genetic algorithm. In: Proceeding IEEE evolutionary computation, Indianapolis, pp 93–96

  22. Ono I, Kobayashi S (1997) A real-coded genetic algorithm for function optimisation using unimodal normal distribution crossover. In: Proceeding of 7th ICGA pp 246–253

  23. Pham DT, Karaboga D (2000) Intelligent optimisation techniques, genetic algorithms, tabu search, simulated annealing and neural networks. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  24. Schwefel HP (1981) Numerical optimisation of computer models. Wiley, Chichester

    Google Scholar 

  25. Srinivas M, Patnaik LM (1994) Genetic algorithms: a survey. IEEE Comput 27(6):17–26

    Google Scholar 

  26. Takahashi M, Kita H (2001) A crossover operator using independent component analysis for real-coded genetic algorithms. In: Proceedings of the congress on evolutionary computation (CEC2001), pp 643–638

  27. Walter DC, Sheble GB (1993) Genetic algorithm solution of economic dispatch with valve point loading. IEEE Trans Power Syst 8:1325–1332

    Article  Google Scholar 

  28. Widrow B, Lehr MA (1990) 30 years of adaptive neural networks: perceptron, madaline, and backpropagation. Proc IEEE 78(9):1415–1442

    Article  Google Scholar 

  29. Yao X (1999) Evolving artificial networks. Proc IEEE 87(7):1423–1447

    Google Scholar 

  30. Yao X, Liu Y (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3(2):82–102

    Article  Google Scholar 

  31. Zurada JM (1992) Introduction to artificial neural systems. West Info Access, Singapore

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

    S. H. Ling & F. H. F. Leung

Authors
  1. S. H. Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. H. F. Leung
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ling, S.H., Leung, F.H.F. An Improved Genetic Algorithm with Average-bound Crossover and Wavelet Mutation Operations. Soft Comput 11, 7–31 (2007). https://doi.org/10.1007/s00500-006-0049-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-006-0049-7

Keywords

Search

Navigation