A Hierarchical Genetic Algorithm Using Multiple Models for Optimization

Abstract

This article presents both the theoretical basis and some experimental results on Hierarchical Genetic Algorithms (HGAs). HGAs are explained in details, along with the advantages conferred by their multi-layered hierarchical topology. This topology is an excellent compromise to the classical exploration/exploitation dilemma. Another feature is the introduction of multiple models for optimization problems, within the frame of an HGA. We show that with such an architecture, it is possible to use a mix of simple models that are very fast and more complex models (with slower solvers), and still achieve the same quality as that obtained with only complex models. The different concepts presented in this paper are then illustrated via experiments on a Computational Fluid Dynamics problem, namely a nozzle reconstruction. The overall results are that a Hierarchical Genetic Algorithm using multiple models can achieve the same quality of results as that of a classic GA using only a complex model, but up to three times faster.