Abstract
Parallel distributed genetic algorithms (PDGA) show better performance in addition to linear-speedup than canonical GA (CGA). However, the detailed analysis of the excellent performance of PDGA has not been performed, and it is uncertain that PDGA outperforms CGA for realistic optimum problems. In this paper, PDGA and CGA applied to solve a typical structural optimization problem to investigate the effectiveness of PDGA. The results of the numerical experiments show that PDGA outperforms CGA for a realistic optimum problems. It is found that PDGA maintains the diversity of the individuals with multiple sup-populations, and the building blocks which are growing in the different sub-populations are combined by the migration.
