Evolutionary process Genetic relatedness 8 For all of the above experiments, we study the effect of genetic relatedness on grouping behavior. In these treatments, groups can be formed in two different ways. In homoge- neous groups, each individual in the current generation of the GA’s population is evaluated separately. During an individual’s fitness evaluation, we fill the group with exact copies of the individual, and the fitness for that individual is the average fitness of all of its copies Since the many eyes hypothesis predicts an inverse relationship between individual vigilance and group size [1,4], we study prey populations across a range of group sizes (5, 10, 25, and 50). In our first experiment, we observe the equilibrium vigilance levels when prey are forced to group. In the next experiment, we relax this assumption and allow the prey to choose to group (or not) every time step. In the latter case, we report the group size as the maximum initial group size. To provide a baseline for the optional grouping experiment, we compare its equilibrium vigilance levels to that of experiments where prey are forced to group and experiments where prey are forced to forage individually. We repeat the evaluation procedure described above until all 100 individuals in the Genetic Algorithm (GA) population have been assigned a fitness (see, e.g., [45] for a full description of GAs). Once all individuals have been assigned a fitness, we use fitness-proportional selec- tion according to a Moran process [46] to produce the next generation’s population of prey. Fitness-proportional selection ensures that prey with higher fitness values generally produce more offspring. The selected prey reproduce asexually, with a small probability of mutations (0.5% per site) affecting their offspring’s genotype. We repeat this evaluation-selection- reproduction process for 2,500 generations to ensure that the GA has reached an evolution- arily stable strategy [47] and replicate the experiments 100 times for each treatment—each with a distinct random number generator seed—to verify that we are capturing evolutionary trends rather than outlier scenarios.