Image　segmentation　remains　one　of　the　major　challenges　in　image　analysis　and　computer　vision.　Fuzzy　clustering,　as　a　soft　segmentation　method,　has　been　widely　studied　and　successfully　applied　in　mage　clustering　and　segmentation.　The　fuzzy　c-means　(FCM)　algorithm　is　the　most　popular　method　used　in　mage　segmentation.　However,　most　clustering　algorithms　such　as　the　k-means　and　the　FCM　clustering　algorithms　search　for　the　final　clusters　values　based　on　the　predetermined　initial　centers.　The　FCM　clustering　algorithms　does　not　consider　the　space　information　of　pixels　and　is　sensitive　to　noise.　In　the　paper,　presents　a　new　fuzzy　c-means　(FCM)　algorithm　with　adaptive　evolutionary　programming　that　provides　image　clustering.　The　features　of　this　algorithm　are:　1)　firstly,　it　need　not　predetermined　initial　centers.　Evolutionary　programming　will　help　FCM　search　for　better　center　and　escape　bad　centers　at　local　minima.　Secondly,　the　spatial　distance　and　the　Euclidean　distance　is　also　considered　in　the　FCM　clustering.　So　this　algorithm　is　more　robust　to　the　noises.　Thirdly,　the　adaptive　evolutionary　programming　is　proposed.　The　mutation　rule　is　adaptively　changed　with　learning　the　useful　knowledge　in　the　evolving　process.　Experiment　results　shows　that　the　new　image　segmentation　algorithm　is　effective.　It　is　providing　robustness　to　noisy　images.