Figure-ground segmentation is a crucial preprocessing step for many image processing and computer vision tasks. Since different object classes need specific segmentation rules, the top-down approach, which learns from the object information, is more suitable to solve segmentation problems than the bottom-up approach. A problem faced by most existing top-down methods is that they require much human work/intervention, meanwhile introducing human bias. As genetic programming (GP) does not require users to specify the structure of solutions, we apply it to evolve segmentors that can conduct the figure-ground segmentation automatically and accurately. This paper aims to determine what kind of image information is necessary for GP to evolve capable segmentors (especially for images with high variations, e.g. varied object shapes or cluttered backgrounds). Therefore, seven different terminal sets are exploited to evolve segmentors, and images from four datasets (bitmap, Brodatz texture, Weizmann and Pascal databases), which are increasingly difficult for segmentation tasks, are selected for testing. Results show that the proposed GP based method can be successfully applied to diverse types of images. In addition, intensity based features are not sufficient for complex images, whereas features containing spectral and statistical information are necessary. Compared with four widely-used segmentation techniques, our method obtains consistently better segmentation performance. (C) 2016 Elsevier B.V. All rights reserved.