University of Surrey

Test tubes in the lab Research in the ATI Dance Research

An evolution strategy assisted by an ensemble of local Gaussian process models

Lu, J, Li, B and Jin, Y (2013) An evolution strategy assisted by an ensemble of local Gaussian process models

[img] Image
p447_published.pdf - ["content_typename_UNSPECIFIED" not defined]
Restricted to Repository staff only
Available under License : See the attached licence file.

Download (876kB)
[img] PDF (licence)
SRI_deposit_agreement.pdf
Restricted to Repository staff only
Available under License : See the attached licence file.

Download (33kB)

Abstract

Surrogate models used in evolutionary algorithms (EAs) aim to reduce computationally expensive objective function evaluations. However, low-quality surrogates may mislead EAs and as a result, surrogate-assisted EAs may fail to locate the global optimum. Among various machine learning models for surrogates, Gaussian Process (GP) models have shown to be effective as GP models are able to provide fitness estimation as well as a confidence level. One weakness of GP models is that the computational cost for training increases rapidly as the number of training samples increases. To reduce the computational cost for training, here we propose to adopt an ensemble of local Gaussian Process models. Different from independent local Gaussian Process models, local Gaussian Process models share the same model parameters. Then the performance of the covariance matrix adaptation evolution strategy (CMA-ES) assisted by an ensemble of local Gaussian Process models with five different sampling strategies is compared. Experiments on eight benchmark functions demonstrate that ensembles of local Gaussian Process models can provide reliable fitness prediction and uncertainty estimation. Among the compared strategies, the clustering technique using the lower confidence bound sampling strategy exhibits the best global search performance. Copyright © 2013 ACM.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
AuthorsEmailORCID
Lu, JUNSPECIFIEDUNSPECIFIED
Li, BUNSPECIFIEDUNSPECIFIED
Jin, YUNSPECIFIEDUNSPECIFIED
Date : 2013
Identification Number : https://doi.org/10.1145/2463372.2463425
Depositing User : Symplectic Elements
Date Deposited : 28 Mar 2017 10:50
Last Modified : 28 Mar 2017 10:50
URI: http://epubs.surrey.ac.uk/id/eprint/806715

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800