Title

An Evolution Strategy with Probabilistic Mutation for Multi-Objective Optimisation

Document Type

Conference Proceeding

Publisher

IEEE

Faculty

Computing, Health and Science

School

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

 

Link to publisher version (DOI)

10.1109/CEC.2003.1299373