Prescott, T.J. orcid.org/0000-0003-4927-5390 and Krubitzer, L. (2018) Evo-devo. In: Prescott, T.J., Lepora, N. and Verschure, P.F.M.J., (eds.) Living machines : A handbook of research in biomimetic and biohybrid systems. Oxford University Press , pp. 82-98. ISBN 9780199674923
Abstract
This chapter explores how principles underlying natural evo-devo (evolution and development) continue to inspire the design of artificial systems from models of cell growth through to simulated three-dimensional evolved creatures. Research on biological evolvability shows that phenotypic outcomes depend on multiple interactions across different organizational levels-the adult organism is the outcome of a series of genetic cascades modulated in time and space by the wider embryological, bodily, and environmental context. This chapter reviews evo-devo principles discovered in biology and explores their potential for improving the evolvability of artificial systems. Biological topics covered include adaptive, selective, and generative mechanisms, and the role of epigenetic processes in creating phenotypic diversity. Modeling approaches include L-systems, Boolean networks, reaction-diffusion processes, genetic algorithms, and artificial embryogeny. A particular focus is on the evolution and development of the mammalian brain and the possibility of designing, using synthetic evo-devo approaches, brain-like control architectures for biomimetic robots.
Metadata
Item Type: | Book Section |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2018 Oxford University Press. This is an author-produced version of a chapter subsequently published in Living machines: A handbook of research in biomimetics and biohybrid systems. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | evo-devo; epigenetics; phenotypic variability; regulatory gene networks; L-systems; Boolean networks; reaction-diffusion process; genetic algorithm; artificial embryogeny |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 02 Dec 2019 11:26 |
Last Modified: | 05 Dec 2019 10:33 |
Status: | Published |
Publisher: | Oxford University Press |
Refereed: | Yes |
Identification Number: | 10.1093/oso/9780199674923.003.0008 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:154069 |