Using the genetic algorithm to generate LISP source code to solve the prisoner's dilemma
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Biological Cybernetics
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Fractally configured neural networks
Neural Networks
Creating artificial neural networks that generalize
Neural Networks
Designing application-specific neural networks using the genetic algorithm
Advances in neural information processing systems 2
Optimisation of artificial neural network structure using genetic techniques on multiple transputers
Proceedings of the world transputer user group (WOTUG) conference on Transputing '91
Automatic definition of modular neural networks
Adaptive Behavior
Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Towards designing artificial neural networks by evolution
Applied Mathematics and Computation - Special issue on articficial life and robotics
Training Product Unit Neural Networks with Genetic Algorithms
IEEE Expert: Intelligent Systems and Their Applications
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Evolving neural networks through augmenting topologies
Evolutionary Computation
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Prediction and Modelling of the Flow of a Typical Urban Basin through Genetic Programming
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Towards the Genetic Synthesisof Neural Networks
Proceedings of the 3rd International Conference on Genetic Algorithms
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
EPNet for Chaotic Time-Series Prediction
SEAL'96 Selected papers from the First Asia-Pacific Conference on Simulated Evolution and Learning
Full Automatic ANN Design: A Genetic Approach
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
GANNet: A Genetic Algorithm for Optimizing Topology and Weights in Neural Network Design
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
Genetic Synthesis of Discrete-Time Recurrent Neural Network
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
On Making Problems Evolutionarily Friendly - Part 1: Evolving the Most Convenient Representations
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Computational modeling of evolutionary learning
Computational modeling of evolutionary learning
Genetic programming neural networks: A powerful bioinformatics tool for human genetics
Applied Soft Computing
Is a learning classifier system a type of neural network?
Evolutionary Computation
Strongly typed genetic programming
Evolutionary Computation
Using genetic programming for artificial neural network development and simplification
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Automatic Design of ANNs by Means of GP for Data Mining Tasks: Iris Flower Classification Problem
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Stochastic optimization for collision selection in high energy physics
IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Hierarchical genetic algorithms operating on populations of computer programs
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
IBM Journal of Research and Development
IBM Journal of Research and Development
On two approaches to image processing algorithm design for binary images using GP
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
A fast multi-output RBF neural network construction method
Neurocomputing
Time series forecast with anticipation using genetic programming
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Evolving artificial neural network ensembles
IEEE Computational Intelligence Magazine
Cooperative coevolution of artificial neural network ensembles for pattern classification
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new methodology of extraction, optimization and application of crisp and fuzzy logical rules
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
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The development of Artificial Neural Networks (ANNs) is traditionally a slow process in which human experts are needed to experiment on different architectural procedures until they find the one that presents the correct results that solve a specific problem. This work describes a new technique that uses Genetic Programming (GP) in order to automatically develop simple ANNs, with a low number of neurons and connections. Experiments have been carried out in order to measure the behavior of the system and also to compare the results obtained using other ANN generation and training methods with evolutionary computation (EC) tools. The obtained results are, in the worst case, at least comparable to existing techniques and, in many cases, substantially better. As explained herein, the system has other important features such as variable discrimination, which provides new information on the problems to be solved.