Distributed Population Models Is Not Only For Parallel But Also For Adapting Plural Environments

Parallelization of a genetic algorithm (GA) is one of the best approach to improve its performance for computationally expensive problems. The distributed genetic algorithm (DGA), which consists of multiple subpopulations of the GA, is one of the well-known model of parallelized GA. In this paper, it is shown that different environments are prepared for each subpopulation and optimization is performed, so that several problems of GA can be solved. This also shows that not only the DGA has excellent parallelism but also flexibility of application to each problem of GA is high. The DGA whose subpopulations have different configurations (referred to as environment) is called the distributed environment genetic algorithm (DEGA). Since many parameters and operations exist in a GA, various environments can be considered in the DEGA. In this paper, we describe the concept of the DEGA and show some examples of implementation. The effectiveness of the proposed DEGA is shown through numerical experiments.