Inside Case-Based Explanation
Inside Case-Based Reasoning
Automatic Construction of Tree-Structural Image Transformations Using Genetic Programming
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitness
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Interactive genetic algorithms with large population and semi-supervised learning
Applied Soft Computing
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This study proposes a cooperative evolutionary optimization method between a user and system (CEUS) for problems involving quantitative and qualitative optimization criteria. In a general interactive evolutionary computation (IEC) model, both the system and user have their own role in the evolution, such as individual reproduction or evaluation. In contrast, the proposed CEUS allows the user to dynamically change the allocation of search roles between the system and user, resulting in simultaneous optimization of qualitative and quantitative objective functions without increasing user fatigue. This is achieved by a combination of user evaluation prediction and the integration of interactive and non-interactive EC. For instance, the system performs a global search at the beginning, the user then intensifies the search area, and finally the system conducts a local search in the intensified search area. This study applies CEUS to an image processing filter design problem that involves both quantitative (filter output accuracy) and qualitative (filter behavior) criteria. Experiments have shown that the proposed CEUS can design image filters in accordance with user preferences, and CEUS interacting with a non-naive user enhanced the initial global search so that it converged and found a reasonable solution more than four times faster than a non-interactive search.