- Using Genetic Algorithm to find the optimal network parameters of Multi-Layer Perceptron for classification tasks
On the easy MNIST dataset, we are able to quickly find a network that reaches > 98% accuracy. On the more challenging CIFAR10 dataset, we get to 56% after 10 generations (with population 20).
To run the brute force algorithm:
python3 brute.py
To run the genetic algorithm:
python3 main.py
You can set your network parameter choices by editing each of those files first. You can also choose whether to use the MNIST or CIFAR10 datasets. Simply set dataset to either mnist or cifar10.
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The genetic algorithm code is based on the code from this excellent blog post: Simple GA
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The small version in: GA and Neural Network
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The big version in: Jan Liphardt's implementation, DeepEvolve.
Have an optimization, idea, suggestion, bug report? Pull requests greatly appreciated!
MIT