Subjective Evaluation on the Method for Reduction of IEC User's Fatigue through Rating-Scale Mapping

Abstract

This paper aims at evaluating the method for reduction of Interactive Evolutionary Computation (IEC) user's fatigue using a rating-scale mapping. The rating-scale mapping method is used to calculate absolute values from relative evaluation values that IEC user rates in each generation and train IEC user's evaluationcharacteristics effectively for accelerating IEC convergence. We first evaluate the method using seven benchmark functions as pseudo IEC users and show the method accelerate IEC convergence. The results of the simulation show that the convergence speed of an IEC using the proposed absolute rating predictor is much faster than using a conventional one. Next, we evaluate the method through subjective test using IEC based CG lighting system in order to prove that the proposed predictor is effective in reducing the user fatigue. The results show that most of the subjects using the predictor can find an optimum solution faster than using the conventional one. Finally, we discuss the results for further improvement.