Abstract
This paper proposes a new method of genetic algorithms (GAs) for discrete optimization problems. For discrete optimization problems, the performance of Distributed GAs (DGAs) are not so good. We propose a new method of increasing the performance of DGAs for discrete optimization problems. The features of the proposed method, Global Crossover based DGA (GCDGA), are multiple crossover operations applied to the elite individuals and DGA without migration. We apply GCDGA to job-shop scheduling problems (JSPs). The experiments on JSPs showed that GCDGA has a better performance than the conventional GAs, and GCDGA provides an efficient distributed scheme in GAs for discrete optimization problems.
