Title

An Evolution Strategy with Probabilistic Mutation for Multi-Objective Optimisation

Document Type

Conference Proceeding

Publisher

IEEE

Faculty

Faculty of Computing, Health and Science

School

School of Nursing and Public Health

RAS ID

1466

Comments

This article was originally published as: Huband, S., Hingston, P., While, L., & Barone, L. (2003, December). An evolution strategy with probabilistic mutation for multi-objective optimisation. In Evolutionary Computation, 2003. CEC'03. The 2003 Congress on (Vol. 4, pp. 2284-2291). IEEE. Original article available here.

Abstract

Evolutionary algorithms have been applied with great success to the difficult field of multi-objective optimisation. Nevertheless, the need for improvements in this field is still strong. We present a new evolutionary algorithm, ESP (the Evolution Strategy with Probabilistic mutation). ESP extends traditional evolution strategies in two principal ways: it applies mutation probabilistically in a GA-like fashion, and it uses a new hypervolume based, parameterless, scaling independent measure for resolving ties during the selection process. ESP outperforms the state-of-the-art algorithms on a suite of benchmark multi-objective test functions using a range of popular metrics.

DOI

10.1109/CEC.2003.1299373

 
COinS
 

Link to publisher version (DOI)

10.1109/CEC.2003.1299373