Using meta-genetic algorithms to tune parameters of genetic algorithms to find lowest energy molecular conformers

Determining the electronic structure of long chain molecules is essential to the understanding of many biological processes, notably those involving molecular receptors in cells. Finding minimum energy conformers and thus electronic structure of long-chain molecules by exhaustive search quickly becomes infeasible as the chain length increases. Typically, resources required are proportional to the number of possible conformers (shapes), which scales as O(3∧L) where L is the length. An optimized genetic algorithm that can determine the minimum energy conformer of an arbitrary long-chain molecule in a feasible time is described, using the tool, PyEvolve. The method is to first solve a generic problem for a long chain by exhaustive search, then by using the pre-determined results in a look-up table, to make use of a Meta-GA to optimize parameters of a simple GA through an evolutionary process to solve that same problem. By comparing the results using the tuned parameters obtained by this method with the results from exhaustive search on several molecules of comparable chain length we have obtained quantitative measurements of an increase in speed by a factor of three over standard parameter settings, and a factor of ten over exhaustive search.