Parallel genetic algorithms for optimizing morphological filters
Peter Kraft · 1995
In this paper several design methods for parallel genetic algorithms (PGAs) optimizing morphological filters are discussed and an optimal scale and machine independent PGA for a loosely coupled, homogeneous or inhomogeneous multiprocessor computer is developed. The optimization is made in terms of computation speed, parallelization efficiency and quality. Quality means, in this context, the fitness of the best chromosome, which is an objective measure for the performance of the corresponding morphological filter in a particular environment. INTRODUCTION AND PROBLEM BACKGROUND Genetic algorithms (GAs) provide a class of robust stochastic search algorithms for various optimization problems. The application of GAs to the design of morphological filters for image processing has recently become an important area of research [Harvey (1), Ehrhardt(2), Chee-Hung (3)]. The standard GA model is based on the evolutionary process found in nature, applied to artificial optimization problems [Hol...