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 ...