Iterative parallel and distributed genetic algorithms with biased initial population

This work proposes an iterative parallel and distributed genetic algorithm with biased initial population to solve large-scale combinatorial optimization problems. The proposed scheme is a master-slave style in which a 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 wide as possible searching by all the slave nodes in the beginning periods 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.