Accelerated learning in layered neural networks
Complex Systems
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Optimizing neural networks using faster, more accurate genetic search
Proceedings of the third international conference on Genetic algorithms
Designing application-specific neural networks using the genetic algorithm
Advances in neural information processing systems 2
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)
Neural network design
Two strategies to avoid overfitting in feedforward networks
Neural Networks
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
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
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
Acceleration Techniques for the Backpropagation Algorithm
Proceedings of the EURASIP Workshop 1990 on Neural Networks
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
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
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The Standard BackPropagation (SBP) algorithm is the most widely known and used learning method for training neural networks. Unfortunately, SBP suffers from several problems such as sensitivity to the initial conditions and very slow convergence. Here we describe how we used Genetic Programming, a search algorithm inspired by Darwinian evolution, to discover new supervised learning algorithms for neural networks which can overcome some of these problems. Comparing our new algorithms with SBP on different problems we show that these are faster, are more stable and have greater feature extracting capabilities.