This paper investigates migration effects of parallel genetic algorithms (GAs) on the line topology of heterogeneous computing resources. Evolution process of parallel GAs is evaluated experimentally on two types of arrangements of heterogeneous computing resources: the ascending and descending order arrangements. Migration effects are evaluated from the viewpoints of scalability, chromosome diversity, migration frequency and solution quality. The results reveal that the performance of parallel GAs strongly depends on the design of the chromosome migration in which we need to consider the arrangement of heterogeneous computing resources, the migration frequency and so on. The results contribute to provide referential scheme of implementation of parallel GAs on heterogeneous computing resources.

This paper proposes an iterative parallel genetic algorithm with biased initial population to solve large-scale combinatorial optimization problems. The proposed scheme employs a master-slave collaboration in which the master node manages searched space of slave nodes and assigns seeds to generate initial population to slaves for their restarting of evolution process. Our approach allows us as widely as possible to search by all the slave nodes in the beginning period of the searching and then focused searching by multiple slaves on a certain spaces that seems to include good quality solutions. Computer experiment shows the effectiveness of our proposed scheme.

In this paper we propose a parallel and distributed computation of genetic local search with irregular topology in distributed environments. The scheme we propose in this paper is implemented with a tree topology established on an irregular network where each computing element carries out genetic local search on its own chromosome set and communicates with its parent when the best solution of each generation is updated. We evaluate the proposed algorithm by a simulation system implemented on a PC-cluster. We test our algorithm on four types topologies: star, line, balanced binary tree and sided binary tree, and investigate the influence of communication topology and delay on the evolution process.