@article{96,
  keywords = {adaptable landscapes, evolution, robotic biology, stochastic dynamics},
  author = {Gao Wang and Trung Phan V and Shengkai Li and Jing Wang and Yan Peng and Guo Chen and Junle Qu and Daniel Goldman and Simon Levin and Kenneth Pienta and Sarah Amend and Robert Austin and Liyu Liu},
  title = {Robots as models of evolving systems.},
  abstract = {<p>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 {\textquotedblleft}survival landscape.{\textquotedblright} 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.</p>},
  year = {2022},
  journal = {Proceedings of the National Academy of Sciences of the United States of America},
  volume = {119},
  pages = {e2120019119},
  month = {03/2022},
  issn = {1091-6490},
  doi = {10.1073/pnas.2120019119},
  language = {eng},
}