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Functions of chemical composition are complex and discrete in nature makingit impossible to optimize them with gradient methods. Genetic algorithms, whichdo not use derivative information, are used to maximize the thermalconductivity of one-dimensional classical harmonic oscillators made from afixed library of randomly generated molecular units. The ability for thegenetic algorithm to build structures with components having no physicalincrement is important in optimizing molecules with a library of unrelatedpolymer units. The performance of genetic algorithms in this problem iscompared with simulated annealing. Hyper-parameters for these routines areselected from a grid search in order to optimize their expected solutionstrength. The solutions found via the genetic algorithm consistently outperformthose of simulated annealing at the cost of longer computer time. Together,these algorithms are able to find thermal conductor candidates that mirrorsolutions in continuous media.

A Comparison of Genetic Algorithms and Simulated Annealing in Maximizing the Thermal Conductivity of Discrete Massive Chains