Strategies for optimizing image processing by genetic and evolutionary computation

We examine the results of 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 others such as conventional methods, neural networks (NNs) and simulated annealing (SA). However, the computation time required is satisfactory in some problems, whereas it is unsatisfactory in others. We consider the current problems of GEC-based methods and present several measures to achieve still better performance