A high-efficient multi-deme genetic algorithm with better load-balance
by Wang Jie; Yuan Jiangjun
International Journal of Computing Science and Mathematics (IJCSM), Vol. 9, No. 3, 2018

Abstract: Genetic algorithm is a very powerful search algorithm that fits for many complex situations. However, it is very time consuming, which limits its usage. Previous work which makes use of multi-core systems to parallelise it performs well and gains much attention. This paper introduces that the load-imbalance problem in parallel genetic algorithm will incur large overhead and will limit the performance. We propose two efficient mechanisms (postponed waiting and work stealing) to achieve fine-grained schedule to solve the problem. Compared with traditional multi-deme parallel genetic algorithm, our high-efficient multi-deme genetic algorithm (HMGA) can achieve an average speedup of 1.36.

Online publication date: Wed, 11-Jul-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computing Science and Mathematics (IJCSM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com