Artificial Life and Robotics

, Volume 2, Issue 1, pp 48–52 | Cite as

A self-organizing genetic algorithm for multimodal function optimization

Original Article

Abstract

A genetic algorithm (GA) has control parameters that must be determined before execution. We propose a self-organizing genetic algorithm (SOGA) as a multimodal function optimizer which sets GA parameters such as population size, crossover probability, and mutation probability adaptively during the execution of a genetic algorithm. In SOGA, GA parameters change according to the fitnesses of individuals. SOGA and other approaches for adapting operator probabilities in GAs are discussed. The validity of the proposed algorithm is verified in simulation examples, including system identification.

Key words

Genetic algorithm Self-organizing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kristinsson K, Dumont GA (1992) System identification and control using genetic algorithms. IEEE Trans Syst Man Cybern 22(5):1033–1046MATHCrossRefGoogle Scholar
  2. 2.
    Jeong IK, Lee JJ (1994) Genetic algorithms and neural networks for identification and control. Proceedings of the first Asian control conference, Japan, pp 697–700Google Scholar
  3. 3.
    Jeong IK, Lee JJ (1995) A modified genetic algorithm for neurocontrollers. IEEE international conference on evolutionary computation, Australia, pp 306–311Google Scholar
  4. 4.
    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, ReadingMATHGoogle Scholar
  5. 5.
    Miller JA, Potter WD, Gandham RV, Lapena CN (1993) An evaluation of local improvement operators for genetic algorithms. IEEE Trans Syst Man Cybern 23(5):1340–1351CrossRefGoogle Scholar
  6. 6.
    Srinivas M, Patnaik LM (1994) Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans Syst Man Cybern 24(4):656–667CrossRefGoogle Scholar
  7. 7.
    Julstrom BA (1995) What have you done for me lately? Adapting operator probabilities in a steady-state genetic algorithm. Proceedings of the 6th international conference on genetic algorithms, pp 81–87Google Scholar
  8. 8.
    Smith J, Fogarty TC (1996) Self adaptation of mutation rates in a steady state genetic algorithm. IEEE International Conference on Evolutionary Computation, Japan, pp 318–323Google Scholar

Copyright information

© ISAROB 1998

Authors and Affiliations

  1. 1.Department of Electrical EngineeringKorea Advanced Institute of Science and TechnologyTaejonKorea

Personalised recommendations