Minimization of Molecular Potential Energy Function Using newly developed Real Coded Genetic Algorithms

The problem of finding the global minimum of molecular potential energy function is very challenging for algorithms which attempt to determine global optimal solution. The principal difficulty in minimizing the molecular potential energy function is that the number of local minima increases exponentially with the size of the molecule. The global minimum of the potential energy of a molecule corresponds to its most stable conformation, which dictates the majority of its properties. In this paper the efficiency of four newly developed real coded genetic algorithms is tested on the molecular potential energy function and their supremacy is established over other existing algorithms. The minimization of the function is performed on an independent set of internal coordinates involving only torsion angles. Computational results with up to 100 degrees of freedom are presented.