Robots as models of evolving systems
Gao Wang, Trung V. Phan, Shengkai Li, Jing Wang, Yan Peng, Guo Chen, Junle Qu, Daniel I. Goldman, Simon A. Levin, Kenneth J. Pienta, Sarah R. Amend, Robert H. Austin, Liyu Liu
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.