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Robots as models of evolving systems
Experimental robobiological physics can bring insights into biological evolution. We present a development of hybrid analog/digital autonomous robots with mutable diploid dominant/recessive 6-byte genomes. The robots are capable of death, rebirth, and breeding. We map the quasi-steady-state survivin...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8944266/ https://www.ncbi.nlm.nih.gov/pubmed/35298335 http://dx.doi.org/10.1073/pnas.2120019119 |
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author | Wang, Gao Phan, Trung V. Li, Shengkai Wang, Jing Peng, Yan Chen, Guo Qu, Junle Goldman, Daniel I. Levin, Simon A. Pienta, Kenneth Amend, Sarah Austin, Robert H. Liu, Liyu |
author_facet | Wang, Gao Phan, Trung V. Li, Shengkai Wang, Jing Peng, Yan Chen, Guo Qu, Junle Goldman, Daniel I. Levin, Simon A. Pienta, Kenneth Amend, Sarah Austin, Robert H. Liu, Liyu |
author_sort | Wang, Gao |
collection | PubMed |
description | Experimental robobiological physics can bring insights into biological evolution. We present a development of hybrid analog/digital autonomous robots with mutable diploid dominant/recessive 6-byte genomes. The robots are capable of death, rebirth, and breeding. We map the quasi-steady-state surviving local density of the robots onto a multidimensional abstract “survival landscape.” We show that robot death in complex, self-adaptive stress landscapes proceeds by a general lowering of the robotic genetic diversity, and that stochastically changing landscapes are the most difficult to survive. |
format | Online Article Text |
id | pubmed-8944266 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-89442662022-09-17 Robots as models of evolving systems Wang, Gao Phan, Trung V. Li, Shengkai Wang, Jing Peng, Yan Chen, Guo Qu, Junle Goldman, Daniel I. Levin, Simon A. Pienta, Kenneth Amend, Sarah Austin, Robert H. Liu, Liyu Proc Natl Acad Sci U S A Physical Sciences Experimental robobiological physics can bring insights into biological evolution. We present a development of hybrid analog/digital autonomous robots with mutable diploid dominant/recessive 6-byte genomes. The robots are capable of death, rebirth, and breeding. We map the quasi-steady-state surviving local density of the robots onto a multidimensional abstract “survival landscape.” We show that robot death in complex, self-adaptive stress landscapes proceeds by a general lowering of the robotic genetic diversity, and that stochastically changing landscapes are the most difficult to survive. National Academy of Sciences 2022-03-17 2022-03-22 /pmc/articles/PMC8944266/ /pubmed/35298335 http://dx.doi.org/10.1073/pnas.2120019119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Physical Sciences Wang, Gao Phan, Trung V. Li, Shengkai Wang, Jing Peng, Yan Chen, Guo Qu, Junle Goldman, Daniel I. Levin, Simon A. Pienta, Kenneth Amend, Sarah Austin, Robert H. Liu, Liyu Robots as models of evolving systems |
title | Robots as models of evolving systems |
title_full | Robots as models of evolving systems |
title_fullStr | Robots as models of evolving systems |
title_full_unstemmed | Robots as models of evolving systems |
title_short | Robots as models of evolving systems |
title_sort | robots as models of evolving systems |
topic | Physical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8944266/ https://www.ncbi.nlm.nih.gov/pubmed/35298335 http://dx.doi.org/10.1073/pnas.2120019119 |
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