Optimization of analytic density functionals by parallel genetic algorithm

We report the construction and implementation of a parallel genetic algorithm (PGA) for use in optimization of analytic density-functional theory. Our new algorithm demonstrates comparable performance to our previous simplex optimizer when benchmarked against the extended G2 set, and the considerable scatter between different optimizations shows that robust methods are required. This new PGA will allow us to further expand our parameter space while efficiently utilizing cluster and supercomputing resources as the problems grow larger.