Strategies for optimizing image processing by genetic and evolutionary computation

We examine the results of major previous attempts to apply genetic and evolutionary computation (GEC) to image processing. In many problems, the accuracy (quality) of solutions obtained by GEC-based methods is better than that obtained by other methods such as neural networks and simulated annealing. However the computation time required is satisfactory in some problems, whereas it is unsatisfactory in other problems. We consider the current problems of GEC-based methods and present the following measures to achieve still better performance: (1) utilizing competent GEC, (2) incorporating other search algorithms such as local hill climbing algorithms, (3) hybridizing with conventional image processing algorithms; (4) modeling the given problem with as smaller parameters as possible, and (5) using parallel processors to evaluate the fitness function