An improved interactive genetic algorithm incorporating relevant feedback

This paper has proposed a new interactive genetic algorithm (IGA) framework incorporating relevant feedback (RF), in which human evaluation is regarded as not only the fitness function of GA, but also the relevant score to instruct interactive machine learning. Thus, on the one hand, user's fatigue, the key issue of IGA, can be alleviated, since some individuals with higher preference weight are added in each generation through relevance feedback technology. On the other hand, the two mapping functions between the low-level parameter space and the high-level users' psychological space can be built during interactions. An instance of this frame, which uses support vector machine (SVM) as the machine learning method in RF, is also provided. The effectiveness of our approach is first evaluated through simulation tests using two benchmark functions. The experimental results show that the convergence speed of the proposal is much faster than that of normal IGA. Then, the approach is applied to retrieve images with emotion semantics queries. The subject experiments also demonstrate that the proposal algorithm can alleviate user fatigue. Furthermore, SVM constructs an individual emotion user model though learning.