Introduction to non-linear optimization
Introduction to non-linear optimization
Accelerated learning in layered neural networks
Complex Systems
Optimizing neural networks using faster, more accurate genetic search
Proceedings of the third international conference on Genetic algorithms
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
On the Problem of Local Minima in Backpropagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Improving the convergence of the back-propagation algorithm
Neural Networks
Initializing back propagation networks with prototypes
Neural Networks
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Methods to speed up error back-propagation learning algorithm
ACM Computing Surveys (CSUR)
Two strategies to avoid overfitting in feedforward networks
Neural Networks
Speeding up backpropagation algorithms by using cross-entropy combined with pattern normalization
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
The Promise of Neural Networks
The Promise of Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Evolving the Topology and the Weights of Neural Networks Using a Dual Representation
Applied Intelligence
An Overview of Evolutionary Computation
ECML '93 Proceedings of the European Conference on Machine Learning
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Contrast enhancement for backpropagation
IEEE Transactions on Neural Networks
Gradient descent learning algorithm overview: a general dynamical systems perspective
IEEE Transactions on Neural Networks
An efficient constrained training algorithm for feedforward networks
IEEE Transactions on Neural Networks
Effect of synthetic emotions on agents’ learning speed and their survivability
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
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The learning method is critical for obtaining good generalisation in neural networks with limited training data. The Standard BackPropagation (SBP) training algorithm suffers from several problems such as sensitivity to the initial conditions and very slow convergence. The aim of this work is to use Genetic Programming (GP) to discover new supervised learning algorithms which can overcome some of these problems. In previous research a new learning algorithms for the output layer has been discovered using GP. By comparing this with SBP on different problems better performance was demonstrated. This paper shows that GP can also discover better learning algorithms for the hidden layers to be used in conjunction with the algorithm previously discovered. Comparing these with SBP on different problems we show they p rovide better performances. This study indicates that there exist many supervised learning algorithms better than SBP and that GP can be used to discover them.