A micro niche evolutionary algorithm with lower-dimensional-search crossover for optimisation problems with constraints Online publication date: Thu, 12-Mar-2009
by Sanyou Zeng, Yang Yang, Yulong Shi, Xianqiang Yang, Bo Xiao, Song Gao, Danping Yu, Zu Yan
International Journal of Bio-Inspired Computation (IJBIC), Vol. 1, No. 3, 2009
Abstract: This paper proposes a micro niche evolutionary algorithm (MNEA) with lower-dimensional-search crossover for optimisation problems with constraints. The best individual in each niche is picked out and all those picked individuals compose the breeding pool of the evolutionary algorithm. Crossover operator of the algorithm searches a lower dimensional space which is determined by the parent points. Both the niche technique and the crossover technique are favourable to enhance the performance of the algorithm. The new algorithm has been tested by the 24 constrained benchmark problems and the results show that it works better than or competitive to any known effective algorithm. Notably, using this new algorithm to solve a well-known engineering problem (pressure vessel problem), its result is much better than that of any other known algorithm.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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 Bio-Inspired Computation (IJBIC):
Login with your Inderscience username and 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
Our Blog 
Follow us on Twitter 
Visit us on Facebook