New Real-Coded Genetic Algorithm Operators for Minimization of Molecular Potential Energy Function

Autor: Abu Bakar Sultan, Md. Nasir Sulaiman, Norwati Mustapha, Bimo Ario Tejo, Siew Mooi Lim
Rok vydání: 2015
Předmět:
Zdroj: Applied Artificial Intelligence. 29:979-991
ISSN: 1087-6545
0883-9514
Popis: The global minimum of the potential energy of a molecule corresponds to its most stable conformation and it dictates most of its properties. Due to the extensive search space and the massive number of local minima that propagate exponentially with molecular size, determining the global minimum of a potential energy function could prove to be significantly challenging. This study demonstrates the application of newly designed real-coded genetic algorithm RCGA called RX-STPM, which incorporates the use of Rayleigh crossover RX and scale-truncated Pareto mutator STPM as defined earlier for minimizing molecular potential energy functions. Computational results for problems with up to 100 degrees of freedom are compared with five other existing methods from the literature. The numerical results indicate the underlying reliability robustness and efficiency of the proposed approach compared to other existing algorithms with low computational costs.
Databáze: OpenAIRE
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