Interactive Evolutionary Computation Using an Absolute Rating Data-Trained Predictor

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.