Localization Of License Plate Number Using Dynamic Image Processing Techniques And Genetic Algorithms In this research, a design of a new genetic algorithm (GA) is introduced to detect the locations of the License Plate (LP) symbols. An adaptive threshold method has been applied to overcome the dynamic changes of illumination conditions when converting the image into binary. Connected component analysis technique (CCAT) is used to detect candidate objects inside the unknown image. A scale-invariant Geometric Relationship Matrix (GRM) has been introduced to model the symbols layout in any LP which simplifies system adaptability when applied in different countries. Moreover, two new crossover operators, based on sorting, have been introduced which greatly improved the convergence speed of the system. Most of CCAT problems such as touching or broken bodies have been minimized by modifying the GA to perform partial match until reaching to an acceptable fitness value. The system has been implemented using MATLAB and various image samples have been experimented to verify the distinction of the proposed system. Encouraging results with 98.4% overall accuracy have been reported for two different datasets having variability in orientation, scaling, plate location, illumination and complex background. Examples of distorted plate images were successfully detected due to the independency on the shape, color, or location of the plate.