Improving the Performance of Predicting Users' Subjective Evaluation Characteristics to Reduce Their Fatigue in TEC

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概要

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• Users' fatigue is the biggest technological hurdle facing Interactive Evolutionary Computation (IEC). This paper introduces the idea of "absolute scale" and "neighbour scale" to improve the performance of predicting users' subjective evaluation characteristics in IEC, and thus it will accelerate EC convergence and reduce users' fatigue. We experimentally evaluate the effect of the proposed method using two benchmark functions. The experimental results show that the convergence speed of IEC using the proposed predictor, which learns from absolute evaluation data, is much faster than the conventional one, which learns from relative data, especially in early generations. Also, IEC with predictors that use recent data are more effective than those which use all past data.

著者

• Wang Shangfei

  Faculty of Design, Kvushu University On leaving from Department of computer Science, University of S

• Takagi Hideyuki

  Faculty of Design, Kyushu University

• Wang Shangfei

  Faculty Of Design Kvushu University On Leaving From Department Of Computer Science University Of Sci

• Takagi Hideyuki

  Faculty Of Design Kyushu University

関連論文

• Improving the Performance of Predicting Users' Subjective Evaluation Characteristics to Reduce Their Fatigue in TEC
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