An improved interactive genetic algorithm incorporating relevant feedback

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S.-F. Wang, X.-F. Wang, and J. Xue: An improved interactive genetic algorithm incorporating relevant feedback. In IEEE ICMLC, volume 5, pages 2996-3001, aug. 2005.



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.

Extended Abstract


author={Shang-Fei Wang and Xu-Fa Wang and Jia Xue},
booktitle={2005 International Conference on Machine Learning and Cybernetics},
title={An improved interactive genetic algorithm incorporating relevant feedback},
pages={2996-3001 Vol. 5},
keywords={genetic algorithms;image retrieval;interactive systems;learning (artificial intelligence);relevance feedback;emotion semantics;fitness function;human evaluation;image retrieval;interactive genetic algorithm;interactive machine learning;preference weight;psychological space;relevant feedback;support vector machine;Fatigue;Feedback;Genetic algorithms;Humans;Machine learning;Proposals;Psychology;Radio frequency;Space technology;Support vector machines;Interactive genetic algorithm;emotion semantics;image retrieval;relevant feedback;support vector machines},
url={ },

Used References

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