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GECCO
2005
Springer
136views Optimization» more  GECCO 2005»
14 years 1 months ago
Learned mutation strategies in genetic programming for evolution and adaptation of simulated snakebot
In this work we propose an approach of incorporating learned mutation strategies (LMS) in genetic programming (GP) employed for evolution and adaptation of locomotion gaits of sim...
Ivan Tanev
GECCO
2009
Springer
194views Optimization» more  GECCO 2009»
14 years 2 months ago
Combining evolution strategy and gradient descent method for discriminative learning of bayesian classifiers
The optimization method is one of key issues in discriminative learning of pattern classifiers. This paper proposes a hybrid approach of the Covariance Matrix Adaptation Evolution...
Xuefeng Chen, Xiabi Liu, Yunde Jia
GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
12 years 11 months ago
Evolution of reward functions for reinforcement learning
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Scott Niekum, Lee Spector, Andrew G. Barto
NCA
2007
IEEE
13 years 7 months ago
Using evolution to improve neural network learning: pitfalls and solutions
: Autonomous neural network systems typically require fast learning and good generalization performance, and there is potentially a trade-off between the two. The use of evolutiona...
John A. Bullinaria
EVOW
2010
Springer
14 years 2 months ago
Learning to Dance through Interactive Evolution
A relatively rare application of artificial intelligence at the nexus of art and music is dance. The impulse shared by all humans to express ourselves through dance represents a u...
Greg A. Dubbin, Kenneth O. Stanley