Parallel genetic algorithms in the optimization of morphological filters: a general design tool

Mathematical morphology has produced an important class of nonlinear filters. Unfortunately, design methods existing for these types of filter tend to be computationally intractable or require some expert knowledge of mathematical morphology. Genetic algorithms (GAs) provide useful tools for optimization problems which are made difficult by substantial complexity and uncertainty. Although genetic algorithms are easy to understand and simple to implement in comparison with deterministic design methods, they tend to require long computation times. But the structure of a genetic algorithm lends itself well to parallel implementation and, by parallelization of the GA, major improvements in computation time can be achieved. A method of morphological filter design using GAs is described, together with an efficient parallelization implementation, which allows the use of massively parallel computers or inhomogeneous clusters of workstations.