The normalized cut (Ncut) method is a popular method for segmenting images and videos. The Ncut method segments an image into two disjoint regions, each segmented by the same method. After the Ncut method has been recursively applied to an image, its final segmented image is obtained. The main drawback of the Ncut method is that a user cannot easily determine the stop criteria because users have no idea about the number of regions in an image. This work proposes the genetic cut (Gcut) algorithm to resolve this shortcoming. Users do need not to specify thresholds in the Gcut algorithm, which automatically segments an image into the proper number of regions. Also, the neighbor-merging (NM) algorithm is proposed for preprocessing the images and improves the performance of the Gcut algorithm. Thus, the proposed Gcut method combines the NM and Gcut algorithms. Furthermore, a heuristic method is proposed to identify a good segment for the Gcut method. In all experiments, the proposed Gcut method outperforms traditional Ncut methods.