Static and adaptive mutation techniques for genetic algorithm: a systematic comparative analysis Online publication date: Fri, 27-Dec-2013
by B.R. Rajakumar
International Journal of Computational Science and Engineering (IJCSE), Vol. 8, No. 2, 2013
Abstract: In this paper, a systematic comparative analysis is presented on various static and adaptive mutation techniques to understand their nature on genetic algorithm. Three most popular random mutation techniques such as uniform mutation, Gaussian mutation and boundary mutation, two recently introduced individual adaptive mutation techniques, a self-adaptive mutation technique and a deterministic mutation technique are taken to carry out the analysis. A common experimental bench of benchmark test functions is used to test the techniques and the results are analysed. The analysis intends to identify a best mutation technique for every benchmark problem and to understand the dependency behaviour of mutation techniques with other genetic algorithm parameters such as population sizes, crossover rates and number of generations. Based on the analytical results, interesting findings are obtained that would improve the performance of genetic 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 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 subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook