ConclusionSegmentation is one of the major steps in image processing without it object recognitionwould be obsolete. The segmentation problem is considered as a hard NPproblem 72 and it cannot be solved with known conventional exhaustive algorithmsand methods. Most of the known methods are either statistical parametricsupervised, or statistical unsupervised methods. The lack of nonparametric unsupervisedmethods to segment different increasing types of complex multicomponentsatellite images led the scientific community to think of metaheuristic algorithms asa solution for the segmentation of these types of multicomponent images. The successof these methods in solving many complex NP problems supported our choiceof Genetic Algorithm (GA) as a metaheuristic algorithm. GA is characterized bybeing efficient and robust in solving many different known NP problems. In thischapter, it is shown that GA performance improves when another heuristic processsuch as Hill-Climbing is added. Moreover, varying the number of cluster centers ineach chromosome increases the algorithm’s efficiency. The suggested combinationof Hill-Climbing with dynamic population improved the performance of GA withrespect of finding the global optimal solution in less time compared to the conventionalGA. Dynamic population of GA is created by varying the number of genesin each chromosome and by adding trivial number to complete every chromosome.This is done in order to ease and simplify the job of crossover and mutation operators.Most of the time in normal GA, avoiding local optimal solution requires furtheriterations as it is proved in many literatures and many experiments. But, sometimethe increase of iterations will lead GA to stick in the local optimal solution 73.Here Hill-Climbing comes handy to slow the convergence to local optimal solutiondue to many factors such as changing the current solution provided by GA. This isdone in order to prevent the metaheuristic algorithm from being stuck in the valley(local optimal solution) according to Hill-Climbing process and moving it slowlytoward the hill (global optimal solution). In addition, the experiments in this chaptercovered the use of many different types of satellite images . It is proved in thischapter that metaheuristic algorithms specifically GA can improve the segmentationprocess such that the accuracy of the segmentation can reach more than 97%.Finally it is suggested that Hybrid Dynamic GA can be further improved to solvemore complex and large images by implementing a parallel version of the algorithm.AcknowledgmentThe AuthorWould like to thank CNRS and the United States Geological Survey forproviding satellite images which are used to prove many concepts in this research.The author would like to extend his gratitude to the Editor and Publisher of thischapter as part of a book.