Asynchronous Parallel Distributed Genetic Algorithm by Layered Server-Client Model
Kazunori Kojima, Masaaki Ishigame, Shozo Makino
The most popular researches about Parallel GAs are implemented as; Population is devided into some subpopulations, each subpopulation executes GA independently and some individuals are migrated in fixed intervals or fixed probability. On the other hand, Grid Computing has been noticed and a research that implements Parallel GA by using Master-Worker model on Grid Computing has been reported. However, on the huge search space problems, Parallel GA by using Master-Worker model needs a lot of worker to get better solution quality. If there are a lot of workers, the traffic loads to the master.
In this paper, we propose Asynchronous Parallel Distributed GA by using Layered Server-Client model. This model is based on Elite Migration on Server-Client model we proposed before. In this model, an Elite Server manages some Subpopulation Clients, and a Master Server manages some Elite Servers. From this structure, the number of Subpopulation Clients that a Elite Server manages is able to be reduced and the traffic on an Elite Server is also able to be reduced. To evaluate our proposed model, we apply to some problems. As the results, we confirm that the fitness is as well as that of current methods and the traffic is less than that of current methods. We also confirm that the migration time is able to be reduced especially in large search space problems.