Genetic learning automata for function optimization

Howell, M. N. and Gordon, Timothy and Brandao, F. V. (2002) Genetic learning automata for function optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 32 (6). pp. 804-815. ISSN 1083-4419

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Item Type:Article
Item Status:Live Archive

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.

Keywords:Boundary value problems, Convergence of numerical methods, Genetic algorithms, Global optimization, Learning systems, Random processes, Learning automata (LA), Automata theory
Subjects:H Engineering > H670 Robotics and Cybernetics
H Engineering > H990 Engineering not elsewhere classified
H Engineering > H650 Systems Engineering
Divisions:College of Science > School of Engineering
ID Code:11673
Deposited On:04 Oct 2013 09:56

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