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.
Highlights
Understanding the evolutionary dynamics of embodied1, locomoting individuals—whether organic, living creatures or mechanical, manufactured robots—is of central importance to studies of living systems and robotic systems alike
These results typify embodied computational evolution (ECE), the employment and study of evolutionary methods using embodied robots, virtual or material—an approach distinct from other evolutionary computation approaches, for which embodiment is not integral. These ECE results provide novel insight into living systems using computational methods; these results may be of interest to evolutionary robotics, for which increased variance may help avoid convergence on local optima (Pugh et al, 2015; Pugh et al, 2016), as we discuss in the Discussion, the fitness cost may not always be worth incurring for the added variance
H, serves as our measure of the genetic variance of the population. This ECE modeling system consists of three bioinspired models that work in concert: 1) a genetic system that encodes morphological and regulatory traits as triplet codons, mutates the genomes, and replicates the genome for reproduction; 2) a developmental system that expresses over time the genome as sets of transcripts, creates random errors in the transcripts, processes those transcripts to create finished parts, and uses a fixed set of rules to assemble the parts into individual robots; and 3) an evolutionary system that tests the behavioral performance of the individuals in a population with a physics simulator and selects the best for reproduction
Summary
Understanding the evolutionary dynamics of embodied, locomoting individuals—whether organic, living creatures or mechanical, manufactured robots—is of central importance to studies of living systems and robotic systems alike. There is, a fitness cost that accompanies the increased variance resulting from masquerading genomes These results typify embodied computational evolution (ECE), the employment and study of evolutionary methods using embodied robots, virtual or material—an approach distinct from other evolutionary computation approaches, for which embodiment is not integral. These ECE results provide novel insight into living systems using computational methods; these results may be of interest to evolutionary robotics, for which increased variance may help avoid convergence on local optima (Pugh et al, 2015; Pugh et al, 2016), as we discuss in the Discussion, the fitness cost may not always be worth incurring for the added variance
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