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Constrained by Design: Influence of Genetic Encodings on Evolved Traits of Robots

Genetic encodings and their particular properties are known to have a strong influence on the success of evolutionary systems. However, the literature has widely focused on studying the effects that encodings have on performance, i.e., fitness-oriented studies. Notably, this anchoring of the literat...

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Autor principal: Miras, Karine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8239187/
https://www.ncbi.nlm.nih.gov/pubmed/34212008
http://dx.doi.org/10.3389/frobt.2021.672379
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author Miras, Karine
author_facet Miras, Karine
author_sort Miras, Karine
collection PubMed
description Genetic encodings and their particular properties are known to have a strong influence on the success of evolutionary systems. However, the literature has widely focused on studying the effects that encodings have on performance, i.e., fitness-oriented studies. Notably, this anchoring of the literature to performance is limiting, considering that performance provides bounded information about the behavior of a robot system. In this paper, we investigate how genetic encodings constrain the space of robot phenotypes and robot behavior. In summary, we demonstrate how two generative encodings of different nature lead to very different robots and discuss these differences. Our principal contributions are creating awareness about robot encoding biases, demonstrating how such biases affect evolved morphological, control, and behavioral traits, and finally scrutinizing the trade-offs among different biases.
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spelling pubmed-82391872021-06-30 Constrained by Design: Influence of Genetic Encodings on Evolved Traits of Robots Miras, Karine Front Robot AI Robotics and AI Genetic encodings and their particular properties are known to have a strong influence on the success of evolutionary systems. However, the literature has widely focused on studying the effects that encodings have on performance, i.e., fitness-oriented studies. Notably, this anchoring of the literature to performance is limiting, considering that performance provides bounded information about the behavior of a robot system. In this paper, we investigate how genetic encodings constrain the space of robot phenotypes and robot behavior. In summary, we demonstrate how two generative encodings of different nature lead to very different robots and discuss these differences. Our principal contributions are creating awareness about robot encoding biases, demonstrating how such biases affect evolved morphological, control, and behavioral traits, and finally scrutinizing the trade-offs among different biases. Frontiers Media S.A. 2021-06-15 /pmc/articles/PMC8239187/ /pubmed/34212008 http://dx.doi.org/10.3389/frobt.2021.672379 Text en Copyright © 2021 Miras. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
Miras, Karine
Constrained by Design: Influence of Genetic Encodings on Evolved Traits of Robots
title Constrained by Design: Influence of Genetic Encodings on Evolved Traits of Robots
title_full Constrained by Design: Influence of Genetic Encodings on Evolved Traits of Robots
title_fullStr Constrained by Design: Influence of Genetic Encodings on Evolved Traits of Robots
title_full_unstemmed Constrained by Design: Influence of Genetic Encodings on Evolved Traits of Robots
title_short Constrained by Design: Influence of Genetic Encodings on Evolved Traits of Robots
title_sort constrained by design: influence of genetic encodings on evolved traits of robots
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8239187/
https://www.ncbi.nlm.nih.gov/pubmed/34212008
http://dx.doi.org/10.3389/frobt.2021.672379
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