Stochastic Ontogenesis in Evolutionary Robotics


This paper investigates the hypothesis that noise in the genotype–phenotype mapping, here called stochastic ontogenesis (SO), is an important consideration in Evolutionary Robotics. This is examined in two ways: first, in the context of seeking to generalise controller performance in an incremental task domain in simulation, and second, in a preliminary study of its effectiveness as a mechanism for crossing the “reality gap” from simulation to physical robots. The performance of evolved neurocontrollers for a fixed-morphology simulated robot is evaluated in both the presence and absence of ontogenic noise, in a task requiring the development of a walking gait that accommodates a varying environment. When SO is applied, evolution of controllers is more effective (replicates achieve higher fitness) and more robust (fewer replicates fail) than evolution using a deterministic mapping. This result is found in a variety of incremental scenarios. For the preliminary study of the utility of SO for moving between simulation and reality, the capacity of evolved controllers to handle unforeseen environmental noise is tested by introducing a stochastic coefficient of friction and evaluating previous populations in the new problem domain. Controllers evolved with deterministic ontogenesis fail to accommodate the new source of noise and show reduced fitness. In contrast, those which experienced ontogenic noise during evolution are not significantly disrupted by the additional noise in the environment. It is argued that SO is a catch-all mechanism for increasing performance of Evolutionary Robotics designs and may have further more general implications for Evolutionary Computation.

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Additional Information: © 2018 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
Event Title: 2018 Conference on Artificial Life
Event Type: Other
Event Dates: 2018-07-23 - 2018-07-27
Last Modified: 27 Dec 2023 10:11
Date Deposited: 12 Sep 2022 16:26
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Related URLs: https://machine ... e2018-final.pdf (Author URL)
https://direct. ... e2018/214/99639 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2018-07-01
Authors: Stanton, Adam



Version: Published Version

License: Creative Commons Attribution

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