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Simple Genetic Algorithm with Local Tuning: Efficient Global Optimizing Technique

Author

Listed:
  • R. Yang

    (University of Manchester)

  • I. Douglas

    (University of Manchester)

Abstract

Genetic algorithms are known to be efficient for global optimizing. However, they are not well suited to perform finely-tuned local searches and are prone to converge prematurely before the best solution has been found. This paper uses genetic diversity measurements to prevent premature convergence and a hybridizing genetic algorithm with simplex downhill method to speed up convergence. Three case studies show the procedure to be efficient, tough, and robust.

Suggested Citation

  • R. Yang & I. Douglas, 1998. "Simple Genetic Algorithm with Local Tuning: Efficient Global Optimizing Technique," Journal of Optimization Theory and Applications, Springer, vol. 98(2), pages 449-465, August.
    • Handle: RePEc:spr:joptap:v:98:y:1998:i:2:d:10.1023_a:1022697719738
    DOI: 10.1023/A:1022697719738
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    Cited by:

    1. C. H. Hsu & W. J. Shyr & K. H. Kuo & P. H. Chou & M. J. Wu, 2010. "Memetic Algorithms for Multiple Interference Cancellations of Linear Array Based on Phase-Amplitude Perturbations," Journal of Optimization Theory and Applications, Springer, vol. 144(3), pages 629-642, March.
    2. Kaelo, P. & Ali, M.M., 2007. "Integrated crossover rules in real coded genetic algorithms," European Journal of Operational Research, Elsevier, vol. 176(1), pages 60-76, January.

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