In this paper we present a prediction process of the Stock Exchange of Thailand index using adaptive evolution strategies. The prediction process does not require the knowledge of the functional form a priori. In each recursion step, genetic algorithm is used to evolve the structure of the prediction function, whereas the coefficient is evolved by evolution strategies. The proposed method has been shown to successfully predict the Stock Exchange of Thailand and returns an error less than 3%. This methodology is also a tool for knowledge discovery in a specific application. We have found that the SET index can be reasonably forecasted with only two factors: the Hang Seng index and Minimum Loan Rate. The proposed method also achieves a lower prediction error when compared with multiple regression method.

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Abstract-The Compact Genetic Algorithm (cGA) has a distinct characteristic that it requires almost minimal memory to store candidate solutions. It represents a population structure as a probability distribution over the set of solutions. Although cGA offers many advantages, it has a limitation that hinges on an assumption of the independency between each individual bit. For example, cGA fails to solve a deceptive function or the so called trap function, which is a standard difficult test problem for genetic algorithm. This paper proposes applying a moving average technique to update a probability vector in the compact genetic algorithm. This method requires fewer evaluations and achieves a higher solution quality. The results are compared with the original cGA, sGA, persistent elitist cGA (pe-cGA) and nonpersistent elitist cGA (ne-cGA). The compared results illustrate that the proposed methodology can successfully improve the solution quality by modifying the updating strategy of cGA.

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