Advances in neural information processing systems 2
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks in the Capital Markets
Neural Networks in the Capital Markets
Evolving neural networks through augmenting topologies
Evolutionary Computation
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Learning Neural Networks for Visual Servoing Using Evolutionary Methods
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Evolutionary reinforcement learning of artificial neural networks
International Journal of Hybrid Intelligent Systems - Hybridization of Intelligent Systems
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
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In this article we present a new method for the pruning of unnecessary connections from neural networks created by an evolutionary algorithm (neuro-evolution). Pruning not only decreases the complexity of the network but also improves the numerical stability of the parameter optimisation process. We show results from experiments where connection pruning is incorporated into EANT2, an evolutionary reinforcement learning algorithm for both the topology and parameters of neural networks. By analysing data from the evolutionary optimisation process that determines the network's parameters, candidate connections for removal are identified without the need for extensive additional calculations.