||Image segmentation is an old and difficult problem. One of the fundamental weaknesses with current image segmentation systems is their inability to adapt the segmentation process as real-worldchanges occur in the image, This dissertation presents the first closed-loop image segmentation system that incorporates genetic algorithms to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions such as time of day, time cf. year, weather, etc. Genetic algorithms efficiently search the hyperspace of segmentation parameter combinations to determine the parameter set that maximizes the segmentation quality measures. This research also explores the hybrid search scheme combining genetic algorithms and a hill climbing and the applicability of the adaptive image segmentation system to the multiobjective optimization problem. The goals of the adaptive image segmentation system are to provide continuous adaptation to normal environmental variations, to exhibit learning capabilities, and to provide robust performance when interacting with a dynamic environment. This dissertation presents experimental results that demonstrate the ability to adapt the segmentation performance in both indoor and outdoor color imagery.