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Content based image retrieval (CBIR) systems aim to provide a means to find pictures in large repositories without using any other information except its contents usually as low-level descriptors. Since these descriptors do not exactly match the high level semantics of the image, assessing perceptual similarity between two pictures using only their feature vectors is not a trivial task. In fact, the ability of a system to induce high level semantic concepts from the feature vector of an image is one of the aspects which most influences its performance. This paper describes a CBIR algorithm which combines relevance feedback, evolutionary computation concepts and ad-hoc strategies in an attempt to fill the existing gap between the high level semantic content of the images and the information provided by the low level descriptors.