A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Explorations in parallel distributed processing: a handbook of models, programs, and exercises
Explorations in parallel distributed processing: a handbook of models, programs, and exercises
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
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
Towards the Genetic Synthesisof Neural Networks
Proceedings of the 3rd International Conference on Genetic Algorithms
Optimizing Neural Networks Using FasterMore Accurate Genetic Search
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 reports several experimental results on the speed of convergence of neural network training using genetic algorithms and back propagation. Recent excitement regarding genetic search lead some researchers to apply it to training neural networks. There are reports on both successful and faulty results, and, unfortunately, no systematic evaluation has been made. This paper reports results of systematic experiments designed to judge whether use of genetic algorithms provides any gain in neural network training over existing methods. Experimental results indicate that genetic search is, at best, equally efficient to faster variants of back propagation in very small scale networks, but far less efficient in larger networks.