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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.
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