How swarm size during evolution impacts the behavior, generalizability, and brain complexity of animats performing a spatial navigation task
This work addresses the problem of understanding optimal group size and social characteristics in evolved swarms for researchers in evolutionary robotics and swarm intelligence, though it is incremental as it builds on existing genetic algorithm and Markov Brain methods.
The study investigated how swarm size during evolution affects animats' behavior, generalizability, and brain complexity in a spatial navigation task, finding that animats evolved in balanced swarms exhibited more flexible behavior, higher fitness across conditions, and increased brain complexity.
While it is relatively easy to imitate and evolve natural swarm behavior in simulations, less is known about the social characteristics of simulated, evolved swarms, such as the optimal (evolutionary) group size, why individuals in a swarm perform certain actions, and how behavior would change in swarms of different sizes. To address these questions, we used a genetic algorithm to evolve animats equipped with Markov Brains in a spatial navigation task that facilitates swarm behavior. The animats' goal was to frequently cross between two rooms without colliding with other animats. Animats were evolved in swarms of various sizes. We then evaluated the task performance and social behavior of the final generation from each evolution when placed with swarms of different sizes in order to evaluate their generalizability across conditions. According to our experiments, we find that swarm size during evolution matters: animats evolved in a balanced swarm developed more flexible behavior, higher fitness across conditions, and, in addition, higher brain complexity.