On the genetic adaptation of stochastic learning automata

Howell, M. N. and Gordon, Timothy (2000) On the genetic adaptation of stochastic learning automata. In: IEEE Conference on Evolutionary Computation, ICEC, 16 - 19 July 2000, La Jolla, CA, USA.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


Both stochastic learning automata and genetic algorithms have previously been shown to have valuable global optimization properties. Learning automata have however been criticized for their perceived 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 escape from local minima. The technique separates the genotype and phenotype properties of the genetic algorithm and has the advantage that the degree of convergence can be quickly ascertained. It also provides the genetic algorithm with a stopping rule and enables bounds to be given on the parameter values obtained.

Keywords:Convergence of numerical methods, Finite automata, Learning algorithms, Optimization, Stochastic learning automata, Genetic algorithms
Subjects:H Engineering > H990 Engineering not elsewhere classified
H Engineering > H650 Systems Engineering
Divisions:College of Science > School of Engineering
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ID Code:11679
Deposited On:04 Oct 2013 10:21

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