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OJBTM
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
ABSTRACT
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.