Self-adaptive routing based on learning classifier systems

Successful computer and internet networks require carefully designed routing protocols. The authors report on their attempt to apply evolutionary computations- that is, to place a learning classifier system on individual routers-to solve routing problems. We found that learning classifier systems are capable of fulfilling traditional routing protocol tasks (e.g., establishing routing tables) after a short period of training. Furthermore, they are capable of adapting to changing network environments and choosing the most efficient path available. Results from our experiments show that the system outperforms shortest path algorithms.