Evaluation of User Fatigue Reduction Through IEC Rating-Scale Mapping

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche


Shangfei Wang and Hideyuki Takagi: Evaluation of User Fatigue Reduction Through IEC Rating-Scale Mapping. The Fourth IEEE International Workshop on Soft Computing as Transdisciplinary Science and Technology (WSTST2005), Muroran, Hokkaido, Japan, Springer-Verlag, pp.672-681 (May25-27, 2005).




We evaluate the convergence speed of an Interactive Evolutionary Computation (IEC) using a rating-scale mapping for user fatigue reduction. First, we introduce the concept of mapping users’ relative ratings to an “absolute scale”; this allows us to improve the performance of the IEC subjective evaluation characteristic predictor, which can in turn accelerate EC convergence and reduce user fatigue. Second, we experimentally evaluate the effectiveness of the proposed method using seven benchmark functions instead of a hunman user. The experimental results show that the convergence speed of an IEC using the proposed absolute rating data-trained predictor is much faster than an IEC using a conventional predictor trained using relative rating data.

Extended Abstract


Used References

Takagi, H. (2001), “Interactive Evolutionary Computation: Fusion of the Capacities of EC Optimization and Human Evaluation,” Proceedings of the IEEE, vol. 89, no. 9, pp.1275–1296. http://dx.doi.org/10.1109/5.949485

Ohsaki, M. and Takagi, H. (1998), “Improvement of Presenting Interface by Predicting the Evaluation Order to Reduce the Burden of Human Interactive EC Operations,” IEEE Int. Conf. on System, Man, and Cybernetics (SMC1998), pp.1284–1289.

Wang, S. F. and Takagi, H. (2005), “Improving the Performance of Predicting Users’ Subjective Evaluation Characteristics to Reduce Their Fatigue in IEC,” J. of Physiological Anthropology Applied Human Science, vol. 24, no. 1, pp.121–125.


Full Text

[extern file]

intern file

Sonstige Links