A new fine-grained evolutionary algorithm based on cellular learning automata
International Journal of Hybrid Intelligent Systems
Engineering Applications of Artificial Intelligence
Information Sciences: an International Journal
Optimization based on a team of automata with binary outputs
Automatica (Journal of IFAC)
Function optimisation by learning automata
Information Sciences: an International Journal
A scale-rate filter selection method in the spectro-temporal domain for phoneme classification
Computers and Electrical Engineering
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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.