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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.

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More information

Published date: 7 July 2021
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: 14 Oct 2021 01:59

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Contributors

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

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