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In this paper, a genetic-based image segmentation method is proposed which optimizes a fuzzy-set-based evaluation function. A K-Means clustering method is used to generate the initial finely segmented image and to reduce the search space of the image segmentation. A genetic algorithm is then employed to control region splitting and merging to optimize the evaluation function. A critical factor affecting the performance of the segmentation is the choice of the evaluation function in the design of genetic algorithm. Here an evaluation function is defined that incorporates both edge and region information. Considering the edge ambiguity in the image, a novel fuzzy-set-based edge-boundary-coincidence measure is defined and combined with a region heterogeneity measure to guide the genetic algorithm to tune the segmentation. Experimental results on test images show that the genetic segmentation algorithm with the fuzzy-set-based evaluation function performs very well.