©1996-2009 All Rights Reserved. Online Journal of Bioinformatics . You may not store these pages in any form except for your own personal use. All other usage or distribution is illegal under international copyright treaties. Permission to use any of these pages in any other way besides the before mentioned must be gained in writing from the publisher. This article is exclusively copyrighted in its entirety to OJB publications. This article may be copied once but may not be, reproduced or re-transmitted without the express permission of the editors. This journal satisfies the refereeing requirements (DEST) for the Higher Education Research Data Collection (Australia). Linking:To link to this page or any pages linking to this page you must link directly to this page only here rather than put up your own page.
Online Journal of Bioinformatics ©
Volume 10 (1):82-92, 2009
Genetic algorithm self-adaptive mutation rate for DNA folding (GASAMR)
Mezher MA1, Khader, AT2
1School of Engineering and Design, School of Computer Science, UB8 3PH, West London, UK Brunel University, 211800, Universiti Sains Malaysia, Penang, Malaysia
Mezher MA, Khader AT., Genetic algorithm self-adaptive mutation rate for DNA folding (GASAMR), Online J Bioinformatics, 10 (1):82-92, 2009. Genetic Algorithm (GA) performance depends greatly on the setting of the GA parameters that control the types and probabilities of application of the genetic operator. However, determining the crossover and mutation operators can be a complex task entails much trial and error. In bioinformatics, GA can be extremely important for optimisation due to the fact that GA is a stochastic search and optimisation technique based on the principles of biological evolution. This paper presents GA with self-adapting mutation rate (SAMR) for solving RNA folding problem. Experimental results demonstrated the effectiveness of this approach by comparing its performance with a traditional GA. The optimisation results are promising based on performance measures: the reliability in finding the optimal solution and the number of generations required for finding the optimal solution.
KEYWORDS: Genetic Algorithms, Self-Adaptive, Mutation Rate, Secondary Structure, Bioinformatics Optimisation, RNA Folding.