In this paper we describe a hybrid evolutionary-cellular automata based algorithm for the segmentation of multidimensional images, in particular hyperspectral images. This algorithm permits automatically generating the cellular automata transition rule set using as training set a group of appropriately generated synthetic RGB images, which greatly simplifies the process given the lack of adequately labeled hyperspectral images. In addition, different types of high dimensional segmentations can be obtained through the regulation of the parameters of the RGB images in the training set. The algorithm has been tested over synthetic and real hyperspectral images and the segmentation results it produces are very competitive when compared to other approaches found in the literature.