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 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.