AUTHOR=Hawthorne-Madell Joshua , Aaron Eric , Livingston Ken , Long John H. TITLE=Embodied Computational Evolution: Feedback Between Development and Evolution in Simulated Biorobots JOURNAL=Frontiers in Robotics and AI VOLUME=8 YEAR=2021 URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2021.674823 DOI=10.3389/frobt.2021.674823 ISSN=2296-9144 ABSTRACT=

Given that selection removes genetic variance from evolving populations, thereby reducing exploration opportunities, it is important to find mechanisms that create genetic variation without the disruption of adapted genes and genomes caused by random mutation. Just such an alternative is offered by random epigenetic error, a developmental process that acts on materials and parts expressed by the genome. In this system of embodied computational evolution, simulated within a physics engine, epigenetic error was instantiated in an explicit genotype-to-phenotype map as transcription error at the initiation of gene expression. The hypothesis was that transcription error would create genetic variance by shielding genes from the direct impact of selection, creating, in the process, masquerading genomes. To test this hypothesis, populations of simulated embodied biorobots and their developmental systems were evolved under steady directional selection as equivalent rates of random mutation and random transcriptional error were covaried systematically in an 11 × 11 fully factorial experimental design. In each of the 121 different experimental conditions (unique combinations of mutation and transcription error), the same set of 10 randomly created replicate populations of 60 individuals were evolved. Selection for the improved locomotor behavior of individuals led to increased mean fitness of populations over 100 generations at nearly all levels and combinations of mutation and transcription error. When the effects of both types of error were partitioned statistically, increasing transcription error was shown to increase the final genetic variance of populations, incurring a fitness cost but acting on variance independently and differently from genetic mutation. Thus, random epigenetic errors in development feed back through selection of individuals with masquerading genomes to the population’s genetic variance over generational time. Random developmental processes offer an additional mechanism for exploration by increasing genetic variation in the face of steady, directional selection.