Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Training feedforward neural networks using genetic algorithms
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
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This paper proposes an evolutionary design methodology of multilayer feedforward neural networks based on constructive approach. We elaborate an adjustable processing element as primitive neuron model. The neural layer can be constructed by assembling several neurons. The multilayer neural network can be finally constructed through cascading several neural layers. The constructive approach facilitates substantially to extract design specifications from a multilayer neural network. Based on the constructive representation of multilayer feedforward neural networks, we use a genetic encoding method, after which the evolution process is elaborated for designing the optimal neural network. The results of our experiments reveal that our methodology is superior to the error back-propagation algorithm both for its executing efficiency and performance.