Double elite co-evolutionary genetic algorithm Online publication date: Wed, 18-Mar-2015
by Xiao-Yan Wang, Quan Liu, Qi-Ming Fu, Le Zhang
International Journal of Computational Science and Engineering (IJCSE), Vol. 6, No. 1/2, 2011
Abstract: A new double elite co-evolutionary genetic algorithm is proposed to avoid the premature convergence and low speed of convergence based on the elite strategy and the concept of co-evolution. In the DECGA, the two different and high fitness individuals (elite individuals) are selected as the core of the evolutionary operation, and the team members are selected by the different evaluation functions to form two teams by these two elite individuals. The two sub-populations can balance the capability of exploration and exploitation by the different evolutionary strategies. Theoretical analysis proves that the algorithm converges to the global optimisation solution. Tests on the functions show that the algorithm can find the global optimal solution for the most test functions, and it can also maintain the population diversity to a certain range. Compared with the existing algorithms, DECGA has a higher performance in precision of convergence and search efficiency.
Online publication date: Wed, 18-Mar-2015
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 Computational Science and Engineering (IJCSE):
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 email@example.com