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  2006, Vol. 19 Issue (3): 417-421    DOI:
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Interactive Evolutionary Computation Using an Absolute Rating DataTrained Predictor
WANG ShangFei, XUE Jia, WANG XuFa
Department of Computer Science and Technology, University of Science and Technology of China, Hefei 230027

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Abstract  Predicting IEC users’ evaluation characteristics is an effective way of reducing users’ fatigue. However, users’ relative evaluation depresses the performance of the algorithm which learns and predicts the users’ evaluation characteristics. The idea of “absolute scale” is introduced to reduce the noise and improve the performance of predicting users’ subjective evaluation characteristics in IEC, thus it accelerates EC convergence and reduces users’ fatigue. Simulation experiments of six benchmark functions are presented to prove the effectiveness of the proposed algorithm. This algorithm is also used in individual emotion fashion image retrieval system. Subjective experimental results of sign tests demonstrate that the proposed algorithm can alleviate users’ fatigue and has a good performance in individual emotional image retrieval.
Key wordsAbsolute Scale      Predictor      Interactive Evolutionary Computation      User Fatigue     
Received: 01 March 2005     
ZTFLH: TP18  
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WANG ShangFei
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WANG ShangFei,XUE Jia,WANG XuFa. Interactive Evolutionary Computation Using an Absolute Rating DataTrained Predictor[J]. , 2006, 19(3): 417-421.
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http://manu46.magtech.com.cn/Jweb_prai/EN/      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2006/V19/I3/417
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