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Howell MN, Gordon TJ, Brandao FV. Genetic learning automata for function optimization. IEEE Trans Syst Man Cybern B Cybern. 2002;32(6):804-15doi: 10.1109/TSMCB.2002.1049614.
Howell, M. N., Gordon, T. J., & Brandao, F. V. (2002). Genetic learning automata for function optimization. IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society, 32(6), 804-15. https://doi.org/10.1109/TSMCB.2002.1049614
Howell, M N, et al. "Genetic learning automata for function optimization." IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society vol. 32,6 (2002): 804-15. doi: https://doi.org/10.1109/TSMCB.2002.1049614
Howell MN, Gordon TJ, Brandao FV. Genetic learning automata for function optimization. IEEE Trans Syst Man Cybern B Cybern. 2002;32(6):804-15. doi: 10.1109/TSMCB.2002.1049614. PMID: 18244886.
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IEEE Trans Syst Man Cybern B Cybern. 2002;32(6):804-15. doi: 10.1109/TSMCB.2002.1049614.

Genetic learning automata for function optimization.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society

M N Howell, T J Gordon, F V Brandao

Affiliations

  1. Dept. of Aeronaut. & Automotive Eng., Loughborough Univ., UK.

PMID: 18244886 DOI: 10.1109/TSMCB.2002.1049614

Abstract

Stochastic learning automata and genetic algorithms (GAs) have previously been shown to have valuable global optimization properties. Learning automata have, however, been criticized for having a relatively slow rate of convergence. In this paper, these two techniques are combined to provide an increase in the rate of convergence for the learning automata and also to improve the chances of escaping local optima. The technique separates the genotype and phenotype properties of the GA and has the advantage that the degree of convergence can be quickly ascertained. It also provides the GA with a stopping rule. If the technique is applied to real-valued function optimization problems, then bounds on the range of the values within which the global optima is expected can be determined throughout the search process. The technique is demonstrated through a number of bit-based and real-valued function optimization examples.

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