KEYWORDS: Genetic algorithms, Optical signal processing, Image processing, Control systems, Computer programming, Genetics, Optimization (mathematics), 3D vision, 3D image processing, Information visualization
Many optical or image processing tasks reduce to the optimization of some set of parameters. Genetic algorithms can optimize these parameters even when the functions they map are fairly complicated, but they can only do so the point where the fitness functions they are given can differentiate between good results and the best result. This can occur when the optimal point is in a region (in a three dimensional example) such as a plateau, where all the surrounding points are of very nearly the same fitness. If there are multiple peaks in close proximity, all of nearly the same fitness but with very deep divides, the algorithm will have trouble 'hopping' from one to the other. One way to overcome these obstacles is to scale the fitness values given by the fitness function, thereby gently modifying the fitness function from the point of view of the algorithm, thus rewarding the more fit solutions to a higher precision than would naturally occur. Four such scaling methods will be compared based upon their handling of a sample set of optical processing data. Success will be determined by comparing the variance over time, selection pressure over time, and best of generation graphs.