Epigenetic Operators and the Evolution of Physically Embodied Robots

Abstract

The genetic operators (GOs) of recombination, mutation, and selection are commonly included in studies of evolution and evolvability, but they are not the only operators that can affect the genotype-to-phenotype (G → P) map and thus the outcomes of evolution. In this paper, we present experiments with an epigenetic operator (EO), interactive wiring of a circuit, alongside common GOs, investigating both epigenetic and GO effects on the evolution of both simulated and physically embodied Braitenberg-inspired robots. As a platform for our experiments, we built a system that encoded the genetics for the physical circuitry of the analog robots and made explicit rules for how that circuitry would be constructed; phenotypic expression consisted of the placement of wires to form the circuitry and thus govern robot behavior. We then varied the presence of gene interactions across populations of robots, studying how the EO-and its effects on G → P maps-affected the results of evolution over several generations. Additionally, a variant of these experiments was run in simulation to provide an independent test of the evolutionary impact of this EO. Our results demonstrate that robot populations with the EO had quantitatively different and potentially less adaptive evolution than populations without it. For example, selection increased the rate at which functional circuitry was lost in the population with the EO, compared to the population without it. In addition, in simulation, EO populations were significantly less fit than populations without it. More generally, results such as these demonstrate the interaction of genetic and EOs during evolution, suggesting the broad importance of including EOs in investigations of evolvability. To our knowledge, our work represents the first physically embodied EO to be used in the evolution of physically embodied robots.

Embodied Computational Evolution: Feedback Between Development and Evolution in Simulated Biorobots

“…Evolutionary biologists have arrived at the same conclusion by comparing genomic with quantitative genetic approaches ( Charlesworth, 2015 ). In response to the limitations of selection and mutation, workers have developed algorithms that search for novelty and diversity ( Lehman and Stanley, 2008 ; Lehman and Stanley, 2011 ; Brawer et al, 2017 ) and introduce stochastic ontogenetic noise into the G-P map ( Stanton, 2018 ). To help understand these alternative evolutionary mechanisms and their interactions with selection and mutation, this current work tested the following hypothesis: Random epigenetic error increases genetic variation in evolving populations.…”

Toward Population-Level Biohybrid Systems: Bioinspiration and Behavior

“…2). While developmental factors have been shown to impact the evolution of robots (Bongard, 2013;Brawer et al 2017), the interaction between mutation and transcriptional errors has not previously been studied. Given that the expression of the genome is the first step in understanding evolutionary dynamics and evolvability (Wagner, 2013), this causal mechanism deserves more attention.…”

Untitled

“…S-CIN will likely culminate with the advent of non-organic synthetic forms of independent cognitive beings capable of the fundamental processes of reproduction and the capacity for selective self-improvement (e.g., Machine Learning) (cf. Brawer et al 2017).…”