  Functions of chemical composition are complex and discrete in nature making
it impossible to optimize them with gradient methods. Genetic algorithms, which
do not use derivative information, are used to maximize the thermal
conductivity of one-dimensional classical harmonic oscillators made from a
fixed library of randomly generated molecular units. The ability for the
genetic algorithm to build structures with components having no physical
increment is important in optimizing molecules with a library of unrelated
polymer units. The performance of genetic algorithms in this problem is
compared with simulated annealing. Hyper-parameters for these routines are
selected from a grid search in order to optimize their expected solution
strength. The solutions found via the genetic algorithm consistently outperform
those of simulated annealing at the cost of longer computer time. Together,
these algorithms are able to find thermal conductor candidates that mirror
solutions in continuous media.
