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

Abstract

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.