Evolutionary Approaches to Expensive Optimisation

TL;DR

This paper reviews surrogate-assisted evolutionary algorithms for expensive optimization problems, discussing key issues, best practices, and solutions for effective approximation and integration in complex, high-dimensional scenarios.

Contribution

It provides a comprehensive analysis of approximation strategies, models, and integration techniques to improve evolutionary algorithms for costly fitness evaluations.

Findings

Identifies effective surrogate models for EAs

Proposes best practices for approximation integration

Highlights methods to ensure reliable fitness approximation

Abstract

Surrogate assisted evolutionary algorithms (EA) are rapidly gaining popularity where applications of EA in complex real world problem domains are concerned. Although EAs are powerful global optimizers, finding optimal solution to complex high dimensional, multimodal problems often require very expensive fitness function evaluations. Needless to say, this could brand any population-based iterative optimization technique to be the most crippling choice to handle such problems. Use of approximate model or surrogates provides a much cheaper option. However, naturally this cheaper option comes with its own price. This paper discusses some of the key issues involved with use of approximation in evolutionary algorithm, possible best practices and solutions. Answers to the following questions have been sought: what type of fitness approximation to be used; which approximation model to use; how to integrate the approximation model in EA; how much approximation to use; and how to ensure reliable approximation.