Only by including expert judgements of items difficulty with other predictors was the accuracy of prediction improved. The search used the validity of neural nets computed during operation of the algorithm to evaluate the efficacy of prediction. The genetic algorithm accomplished some improved prediction of item difficulties in the estimation, but no improvement with regard to discrimination. This examination used the validation sample. In sum, application of the complex and computer-intensive neural nets and genetic algorithms revealed no advantage over linear methods for predicting item difficulty and item discrimination statistics for GRE analytical reasoning items.