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

TL;DR

This paper compares genetic algorithms and simulated annealing for optimizing the thermal conductivity of discrete molecular chains, finding that genetic algorithms outperform simulated annealing in solution quality but require more computation.

Contribution

It introduces the application of genetic algorithms to optimize discrete molecular structures for thermal conductivity, highlighting their advantages over simulated annealing.

Findings

Genetic algorithms outperform simulated annealing in solution quality.

Genetic algorithms require longer computational time.

Both algorithms find solutions similar to continuous media models.

Abstract

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.