GEESE: Grammatical Evolution Algorithm for Evolution of Swarm Behaviors

摘要: Animals such as bees, ants, birds, fish, and others are able to perform complex coordinated tasks like foraging, nest-selection, flocking escaping predators efficiently without centralized control or coordination. Conventionally, mimicking these behaviors with robots requires researchers study actual behaviors, derive mathematical models, implement models algorithms. We propose a distributed algorithm, Grammatical Evolution algorithm for of Swarm bEhaviors (GEESE), which uses genetic methods generate collective robot swarms. GEESE grammatical evolution evolve primitive set human-provided rules into productive individual behaviors. The is evaluated in two different ways. First, compared state-of-the-art algorithms on the canonical Santa Fe Trail problem. Results show that outperforms by (a) providing better solution quality given sufficient population size while (b) utilizing fewer evolutionary steps. Second, both hand-coded Evolution-generated swarm foraging task.