Image ordering by cellular genetic algorithms with TSP and ICA

We have studied the use of cellular automata and cellular genetic algorithms for the image classification and ordering problems. The cellular genetic algorithm is a genetic algorithm that has similarities with cellular automata. Image distances are measured as a number of needed cellular GA transforms, when morphing from image to image. Images distances are given to the traveling salesman solver, which orders the images to the shortest route order. The preliminary results seem to support the hypothesis that in principle this kind of image ordering and classification method works. The drawback of the proposed method is a large amount of calculations and the needed when we are testing each image against every other image. Independent component analysis is used in order to construct 3D model of how the tested images are located in space relative to each other.