Multi-Agent Learning with a Distributed Genetic Algorithm

Lightweight agents distributed in space have the potential to solve many complex problems. In this paper, we examine a model where agents represent individuals in a genetic algorithm (GA) solving a shared problem. We examine two questions: (1) How does the network density of connections between agents a ffect the performance of the systems? (2) How does the interaction topology a affect the performance of the system? In our model, agents exist in either a random network topology with long-distance communication, or a location-based topology, where agents only communicate with near neighbors. We examine both fixed and dynamic networks. Within the context of our investigation, our initial results indicate that relatively low network density achieves the same results as a panmictic, or fully connected, population. Additionally, we find that dynamic networks outperform fixed networks, and that random network topologies outperform proximity-based network topologies. We conclude by showing how this model can be useful not only for multi-agent learning, but also for genetic algorithms, agent-based simulation and models of diff usion of innovation