Classification and fusion are major tasks in many applications and in particular for automatic semantic-based video content indexing and retrieval. In this paper, we focus on the challenging task of classifier output fusion. It is a necessary step to efficiently estimate the semantic content of video shots from multiple cues. We propose to fuse the numeric information provided by multiple classifiers in the framework of possibility logic. In this framework, many operators with different properties were suggested to achieve the fusion. We present a binary tree structure to model the fusion mechanism of available cues and the genetic algorithms that are used to determine the most appropriate operators and fusion tree structure. Experiments are conducted in the framework of TRECVID feature extraction task that consists in ordering shots with respect to their relevance to a given class. Finally, we will show the efficiency of our approach.