The University of Southampton
University of Southampton Institutional Repository

On the use of feature-maps for improved quality-diversity meta-evolution

On the use of feature-maps for improved quality-diversity meta-evolution
On the use of feature-maps for improved quality-diversity meta-evolution
Quality-Diversity (QD) algorithms evolve a behaviourally diverse archive of high-performing solutions. In QD meta-evolution, one evolves a population of QD algorithms by modifying algorithmic components (e.g., the behaviour space) to optimise an archive-level objective, the meta-fitness. This paper investigates which feature-map is best for defining the behaviour space for an 8-joint robot arm. Meta-evolution with non-linear feature-maps yields a 15-fold meta-fitness improvement over linear feature-maps. On a damage recovery test, archives evolved with non-linear feature-maps outperform traditional MAP-Elites variants.
damage recovery, evolutionary robotics, meta-evolution, quality-diversity algorithms, representational capacity
83-84
Bossens, David
633a4d28-2e59-4343-98fe-283082ba1873
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4
Bossens, David
633a4d28-2e59-4343-98fe-283082ba1873
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4

Bossens, David and Tarapore, Danesh (2021) On the use of feature-maps for improved quality-diversity meta-evolution. In GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion. pp. 83-84 . (doi:10.1145/3449726.3459442).

Record type: Conference or Workshop Item (Paper)

Abstract

Quality-Diversity (QD) algorithms evolve a behaviourally diverse archive of high-performing solutions. In QD meta-evolution, one evolves a population of QD algorithms by modifying algorithmic components (e.g., the behaviour space) to optimise an archive-level objective, the meta-fitness. This paper investigates which feature-map is best for defining the behaviour space for an 8-joint robot arm. Meta-evolution with non-linear feature-maps yields a 15-fold meta-fitness improvement over linear feature-maps. On a damage recovery test, archives evolved with non-linear feature-maps outperform traditional MAP-Elites variants.

This record has no associated files available for download.

More information

Published date: 7 July 2021
Additional Information: Funding Information: This work was supported by EPSRC under the New Investigator Award grant, EP/R030073/1 (Tarapore). The authors acknowledge the IRIDIS High Performance Computing Facility and thank Arnold Benedict for initial work on the simulator. Publisher Copyright: © 2021 Owner/Author.
Keywords: damage recovery, evolutionary robotics, meta-evolution, quality-diversity algorithms, representational capacity

Identifiers

Local EPrints ID: 450389
URI: http://eprints.soton.ac.uk/id/eprint/450389
PURE UUID: e998a130-60a3-44b0-a2ca-f11d5df6d03b
ORCID for David Bossens: ORCID iD orcid.org/0000-0003-1924-5756
ORCID for Danesh Tarapore: ORCID iD orcid.org/0000-0002-3226-6861

Catalogue record

Date deposited: 27 Jul 2021 16:30
Last modified: 17 Mar 2024 03:46

Export record

Altmetrics

Contributors

Author: David Bossens ORCID iD
Author: Danesh Tarapore ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×