TY - GEN
T1 - Learning what to ignore: Memetic climbing in topology and weight space
AU - Togelius, Julian
AU - Gomez, Faustino
AU - Schmidhuber, Jürgen
N1 - Generated from Scopus record by KAUST IRTS on 2022-09-14
PY - 2008/11/14
Y1 - 2008/11/14
N2 - We present the memetic climber, a simple search algorithm that learns topology and weights of neural networks on different time scales. When applied to the problem of learning control for a simulated racing task with carefully selected inputs to the neural network, the memetie climber outperforms a standard hill-climber. When inputs to the network are less carefully selected, the difference is drastic. We also present two variations of the memetie climber and discuss the generalization of the underlying principle to population-based neuroevolution algorithms. © 2008 IEEE.
AB - We present the memetic climber, a simple search algorithm that learns topology and weights of neural networks on different time scales. When applied to the problem of learning control for a simulated racing task with carefully selected inputs to the neural network, the memetie climber outperforms a standard hill-climber. When inputs to the network are less carefully selected, the difference is drastic. We also present two variations of the memetie climber and discuss the generalization of the underlying principle to population-based neuroevolution algorithms. © 2008 IEEE.
UR - http://ieeexplore.ieee.org/document/4631241/
UR - http://www.scopus.com/inward/record.url?scp=55749093699&partnerID=8YFLogxK
U2 - 10.1109/CEC.2008.4631241
DO - 10.1109/CEC.2008.4631241
M3 - Conference contribution
SN - 9781424418237
SP - 3274
EP - 3281
BT - 2008 IEEE Congress on Evolutionary Computation, CEC 2008
ER -