From Darwin to Sommerfeld: Genetic algorithms and the electron gas

TL;DR

This paper explores the application of genetic algorithms to model the homogeneous electron gas, demonstrating a novel interdisciplinary approach that leverages evolutionary computation for physical systems analysis.

Contribution

It introduces a new method using genetic algorithms to describe the homogeneous electron gas, bridging evolutionary algorithms and condensed matter physics.

Findings

Genetic algorithms can effectively model the homogeneous electron gas.

The approach offers a new computational tool for physics problems.

Potential for broader application in physical systems modeling.

Abstract

In return for the long-standing contributions of Physics to Biology, now the inverse way is frequently traveled through in order to think about many physics phenomena. In this vein, evolutionary algorithms, particularly genetic algorithms, are being more and more used as a tool to deal with several Physics problems. Here, we show how to apply a genetic algorithm to describe the homogeneous electron gas.