Stochastic Ontogenesis in Evolutionary Robotics

Adam Stanton

    Research output: Chapter in Book/Published conference outputConference publication


    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.
    Original languageEnglish
    Title of host publicationThe 2018 Conference on Artificial Life
    Number of pages8
    Publication statusPublished - 1 Jul 2018
    Event2018 Conference on Artificial Life: Beyond AI - Tokyo, Japan
    Duration: 23 Jul 201827 Jul 2018


    Conference2018 Conference on Artificial Life
    Abbreviated titleALIFE 2018

    Bibliographical note

    © 2018 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.


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