Robots as models of evolving systems.

Wang, Gao; Phan, Trung V; Li, Shengkai; Wang, Jing; Peng, Yan; Chen, Guo; Qu, Junle; Goldman, Daniel I et al. · Proc Natl Acad Sci U S A · 2022

basic_science · Level V

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Abstract

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.

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