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Content based image retrieval (CBIR) systems work by retrieving images which are related to the query image (QI) from huge databases. The available CBIR systems extract limited feature sets which confine the retrieval efficacy. In this work, extensive robust and important features were extracted from the images database and then stored in the feature repository. This feature set is composed of color signature with the shape and color texture features. Where, features are extracted from the given QI in the similar fashion. In this paper, we introduce a new approach to image retrieval. We proposed a novel great spate algorithm used to find content based images in large datasets like the images that has the highest similarity with the QI from a da tabase. During the CBIR process every image in the database is indicated by chromosome, from the QI the color signature, shape and color texture are extracted and also from the chromosomes that were generated. The next step is calculating fitness function for every chromosome using similarity difference equation. The great spate algorithm when inserted in the genetic algorithm was an effective way to yield a good solution instead of using the genetic algorithm alone. Our proposed CBIR system is assessed by inquiring number of images from the test dataset and the efficiency of the system is evaluated by calculating precision-recall value for the results. The results were superior to other state-of-the-art CBIR systems in regard to precision.