Abstract
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.
(On leave from the Department of Computer Science, University of Science and Technology of China)
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Reference
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.
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.
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© 2005 Springer-Verlag Berlin Heidelberg
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Wang, S., Takagi, H. (2005). Evaluation of User Fatigue Reduction Through IEC Rating-Scale Mapping. In: Abraham, A., Dote, Y., Furuhashi, T., Köppen, M., Ohuchi, A., Ohsawa, Y. (eds) Soft Computing as Transdisciplinary Science and Technology. Advances in Soft Computing, vol 29. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32391-0_72
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DOI: https://doi.org/10.1007/3-540-32391-0_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25055-5
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