In this paper, we present an approach to image segmentation in which user selected sets of examples and counter-examples supply information about the specific segmentation problem. Image segmentation is guided by a genetic algorithm, which learns the appropriate subset and spatial combination of a collection of discriminating functions, associated with image features. The genetic algorithm encodes discriminating functions into a functional template representation, which can be applied to the input image to produce candidate segmentation. The quality of each segmentation is evaluated within the genetic algorithm, by a comparison of two physics-based techniques for region growing and edge detection. Experimental results on real SAR imagery demonstrate that evolved segmentations are consistently better than segmentations derived from the Bayesian best single feature.