基于绝对尺度预测的交互式进化算法

Interactive Evolutionary Computation Using an Absolute Rating Data-Trained Predictor

预测用户的评估特性可以有效减轻交互式进化算法中的用户疲劳问题,但基于相对尺度的用户评估制约了预测的准确性.针对这一问题.本文提出一种基于绝对尺度预测的交互式进化算法,将用户的相对评估转化成绝对评估,减少预测器学习样本中的噪声,提高预测的准确性,从而加快算法的收敛速度,更好地减轻用户疲劳.文中采用6个标准函数模拟用户.验证算法的有效性.将该算法应用于服装图像的个性化情感检索,运用符号检验方法证实采用本文所提出的算法可以获得更好的检索结果.

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 1EC, 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.