Genetic interval neural networks for granular data regression
Information Sciences: an International Journal
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Interval optimization algorithms usually use local search methods to obtain a good upper bound of the global optimal value. These local methods are based on point evaluations. A new interval-genetic algorithm is presented that combines an interval arithmetic and a genetic algorithm in the paper. The proposed algorithm uses the improved upper bound of the global optimal value obtained by the genetic algorithm to delete the intervals not containing the global optimal solution from the work set at each iteration. Using the interval arithmetic, the new algorithm not only has the advantages of simplicity and less knowledge about problems as traditional interval optimization algorithms, but also produces the reliable domains where the genetic algorithm is applied to search. Moreover, with the direction provided by the genetic algorithm applied, the chance to divide the reliable interval is increased. A convergence is proved and numerical experiments shows that the proposed algorithm is more efficient than traditional interval optimization algorithms.