Epistatic gene interactions in the evolution of robots

The evolution of robots has promoted both technological development and biological inquiry, but little to no effort has been placed on exploring gene interactions within evolving artificial genetic encodings. Studying gene interactions is fundamental to understanding how biological systems function, and the same applies to artificial evolvable systems. As autonomous systems become more complex, reliance on black-box encodings—where it is unclear how the genome produces phenotypes—poses limitations for efficacy, explainability, and safety. This study investigates epistatic gene interactions—a non-additive effect of two gene knockouts on a trait–in the evolution of robots encoded with artificial Gene Regulatory Networks. We evolve robot populations in a physics-based simulation and apply techniques from experimental biology to identify and quantify the evolution of epistasis in these robotic systems. The main contribution of this work is to demonstrate how epistasis can be quantified and analyzed to reveal insights into artificial genotype–phenotype mappings. The results show that selection pressure influences epistasis, though further studies are needed to determine whether epistasis itself is being favored or if it emerges as a by-product of selection acting on morphology and control.