Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic micro programming of neural networks
Advances in genetic programming
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
Towards the Genetic Synthesisof Neural Networks
Proceedings of the 3rd International Conference on Genetic Algorithms
Full Automatic ANN Design: A Genetic Approach
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
Strongly typed genetic programming
Evolutionary Computation
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
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Using genetic algorithms in software optimization
TELE-INFO'07 Proceedings of the 6th WSEAS Int. Conference on Telecommunications and Informatics
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
Automatic Recurrent ANN development for signal classification: detection of seizures in EEGs
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
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The creation process of Artificial Neural Networks (ANNs) used to be quite slow and the human expert had to test several architectures until finding the one that achieves the best results for the solution of a certain problem. This work presents a new technique that uses Genetic Programming (GP) for automatically creating ANNs. This technique also allows the obtaining of simplified networks with few neurons for solving the problem. In order to measure the performance of the system and to compare the results with other ANN generation and training methods with Evolutionary Computation (EC) techniques, several tests were performed with problems based on some of the most used test databases. The results of those comparisons showed that the system achieved good results comparable with already existing techniques and, in most of the cases, they worked better than those techniques.