TY - GEN
T1 - Self-adaptive routing based on learning classifier systems
AU - Huang, Chung Yuan
AU - Sun, Chuen-Tsai
PY - 2004/9/13
Y1 - 2004/9/13
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=4344648091&partnerID=8YFLogxK
U2 - 10.1109/CEC.2004.1330924
DO - 10.1109/CEC.2004.1330924
M3 - Conference contribution
AN - SCOPUS:4344648091
SN - 0780385152
SN - 9780780385153
T3 - Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004
SP - 678
EP - 682
BT - Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004
Y2 - 19 June 2004 through 23 June 2004
ER -