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Biomedical image registration or geometrical alignment of 2-D/2-D image data is increasingly important in diagnosis, treatment planning, computer-guided therapies and in biomedical research. In this paper we present affine registration of same modality images and different (MR & CT) modality images. Automatic registration is achieved by maximization of a similarity metric, which is mutual information (MI) or relative entropy, based on the concept of information theory. Registration based on MI usually requires an optimization technique to achieve correctly aligned images. There exist many optimization schemes, most of which are local and require a starting point. Unfortunately the functions of similarity metric used in the present problem are nonconvex and irregular and therefore global methods are often required. In this paper, we have implemented genetic algorithm as an optimization technique to overcome these problems. Experimental results show our algorithm is a robust and efficient method.