Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
