Clustering-based hierarchical genetic algorithm for complex fitness landscapes
by Rahul Kala, Anupam Shukla, Ritu Tiwari
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 9, No. 2, 2010

Abstract: We propose the use of a hierarchical genetic algorithm (GA) for optimisation in complex landscapes. While the slave GA tries to find the local optima in the restricted fitness landscape of low complexity, the master GA tries to identify interesting regions in the entire landscape. The slave GA is a conventional GA with high convergence. The master GA is more exploratory in nature. This GA clusters the fitness landscape with each cluster in control of a slave GA. The number of clusters decreases with time to get global characteristics. The novelty of the suggested approach lies in the trade-off between the search for global optima and convergence to local optima that can be controlled between the two GAs. We tested the algorithm and observed that the approach exceeds conventional GA as well as particle swarm optimisation in complex landscapes.

Online publication date: Sat, 31-Jul-2010

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 Intelligent Systems Technologies and Applications (IJISTA):
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