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This paper proposes to display individuals of interactive evolutionary computation(EC) in an evaluation order to reduce the fatigue of human operators. To display in an evaluation order, two predictio...n methods which do not depend on application tasks-the first using a neural networks(NN), and the second using Euclidean distance measure-are proposed. The prediction method based on an NN learns the relationship between past individuals and their evaluation values that an interactive EC operator gave in the past EC generations. The prediction method based on Euclidean distance measure predicts the human evaluation to a given individual using a weighting average method of the evaluation values to individuals in the past EC generations. These weights are calculated by the Euclidean distances between the given individual and individuals in the past. We evaluate the performances of these two prediction methods through simulation and subjective tests. The simulation test evaluates how the predicted order is closer to the actual one than similarity between the order that GA produces-conventional order-and the actual one. The comparison of cross-correlations between these two similarities has shown that the proposed method is significantly better than the conventional method. Following the simulation, the subjective tests are conducted to test how the proposed method let human operators feel better than the conventional method. The result has shown that there is no significant difference between the proposed method and the conventional method unlikely to the simulation test. We discuss the results of simulation and subjective tests. Furthermore, we discuss four hints for the future improvement obtained through subjective tests and the reports from subjects.続きを見る
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