Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Accelerating neuroevolutionary methods using a Kalman filter
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolving neural networks in compressed weight space
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Efficient non-linear control through neuroevolution
ECML'06 Proceedings of the 17th European conference on Machine Learning
IEEE Transactions on Information Theory
Neural Networks
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We present a novel method of reducing the training time by learning parameters of a model at hand in compressed parameter space. In compressed parameter space the parameters of the model are represented by fewer parameters, and hence training can be faster. After training, the parameters of the model can be generated from the parameters in compressed parameter space. We show that for supervised learning, learning the parameters of a model in compressed parameter space is equivalent to learning parameters of the model in compressed input space. We have applied our method to a supervised learning domain and show that a solution can be obtained at much faster speed than learning in uncompressed parameter space. For reinforcement learning, we show empirically that searching directly the parameters of a policy in compressed parameter space accelerates learning.