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.