Genetic algorithm dynamics on a rugged landscape

TL;DR

This paper introduces a statistical mechanics model for genetic algorithm dynamics on rugged landscapes, comparing it to existing models and applying it to the NK model to better understand optimization behavior.

Contribution

It presents a new general model based on parent-child fitness correlation, extending previous approaches and applicable to complex fitness landscapes.

Findings

Model aligns with genetic algorithm behavior on rugged landscapes

Comparison shows advantages over maximum entropy models

Application to NK model demonstrates practical utility

Abstract

The genetic algorithm is an optimization procedure motivated by biological evolution and is successfully applied to optimization problems in different areas. A statistical mechanics model for its dynamics is proposed based on the parent-child fitness correlation of the genetic operators, making it applicable to general fitness landscapes. It is compared to a recent model based on a maximum entropy ansatz. Finally it is applied to modeling the dynamics of a genetic algorithm on the rugged fitness landscape of the NK model.