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Combining with genetic algorithm, the improved estimation of distribution algorithm (EDA) is provided. The crossover and mutation operations are added and the "elite" individuals are retained, which can keep the excellent evolution mode. The selection based on energy entropy is added, which can explore the solution space sufficiently and keep the population diversity. A neural network with switches introduced to its links is proposed. The method of tuning the structure and parameters of the neural network using the improved EDA is provided. The carrying robot inverse dynamics model approximation example show the validity of this algorithm.