A Summary of Research on Parallel Genetic Algorithms

The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We present an extension to previous categorizations of the parallelization techniques used in this field. We will use this categorization to guide us through a review of many of the most important publications. We will build on this survey to try to identify some of the problems that have not been studied systematically yet. 1 Introduction Genetic Algorithms (GAs) are efficient search methods based on principles of natural selection and population genetics. They are being successfully applied to problems in business, engineering and science (Goldberg, 1994). GAs use randomized operators operating over a population of candidate solutions to generate new points in the search space. In the past few years, parallel genetic algorithms (PGAs) have been used to solve difficult problems. Hard problems need a bigger population and this translates directly into higher computational costs. The basic..