Emergence of Polymorphic Mating Strategies in Robot Colonies

Polymorphism has fascinated evolutionary biologists since the time of Darwin. Biologists have observed discrete alternative mating strategies in many different species. In this study, we demonstrate that polymorphic mating strategies can emerge in a colony of hermaphrodite robots. We used a survival and reproduction task where the robots maintained their energy levels by capturing energy sources and physically exchanged genotypes for the reproduction of offspring. The reproductive success was dependent on the individuals'' energy levels, which created a natural trade-off between the time invested in maintaining a high energy level and the time invested in attracting mating partners. We performed experiments in environments with different density of energy sources and observed a variety in the mating behavior when a robot could see both an energy source and a potential mating partner. The individuals could be classified into two phenotypes: 1) forager, who always chooses to capture energy sources, and 2) tracker, who keeps track of potential mating partners if its energy level is above a threshold. In four out of the seven highest fitness populations in different environments, we found subpopulations with distinct differences in genotype and in behavioral phenotype. We analyzed the fitnesses of the foragers and the trackers by sampling them from each subpopulation and mixing with different ratios in a population. The fitness curves for the two subpopulations crossed at about 25% of foragers in the population, showing the evolutionary stability of the polymorphism. In one of those polymorphic populations, the trackers were further split into two subpopulations: (strong trackers) and (weak trackers). Our analyses show that the population consisting of three phenotypes also constituted several stable polymorphic evolutionarily stable states. To our knowledge, our study is the first to demonstrate the emergence of polymorphic evolutionarily stable strategies within a robot evolution framework.