Selection of Item Response Model by Genetic Algorithm

Abstract

A method of selecting an item response model with a genetic algorithm is proposed, where a model indicator variable is regarded as a chromosome to distinguish other individuals. This scheme enables a model for each item to be selected automatically. The genetic algorithm with the set of techniques that is implemented here is called the simple genetic algorithm, and the results obtained from simulation studies were satisfactory. An issue with the graded response model and the generalized partial credit model was examined using simulation studies and numerical examples was to find which was the more useful of these two prevailing kinds. The results obtained from simulation studies proved the graded response model fit the data more flexibly, since it fit the data generated under the generalized partial credit model more frequently than for the opposite case. However, the generalized partial credit model was more suitable for two real data sets.