Parallel Population Models for Genetic Algorithms
نویسنده
چکیده
This paper is an attempt to make the discussion of parallel genetic algorithms independent from hardware issues. There have been many parallel implementations of genetic algorithms, some of them on hardware that is not even available any more. Most of these implementations have also modified the structure of the genetic algorithm for matters of efficiency, and it has been reported that these modifications have also improved the quality of genetic search. It is thus also desirable to discuss parallel genetic algorithms also in a hardware independent context. In this paper, parallel implementations of genetic algorithms are reviewed and their parallel population model is extracted. They are classified due to the radius of their mating operator into ‘global’, ‘regional’ and ‘local’ models. Based on these considerations, a flexible parallel population model for genetic algorithms is derived, which contains all the above models as a special case and could nevertheless be implemented on many parallel architectures.
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