We propose a method for learning the neural network architecture that based on Genetic Algorithm (GA). Our approach uses a genetic algorithm integrated with standard Stochastic Gradient Descent(SGD) which allows the sharing of weights across all architecture solutions. The method uses GA to design a sub-graph of Convolution cell which maximizes the accuracy on the validation-set. Through experiments, we demonstrate this methods performance on both CIFAR10 and CIFAR100 dataset with an accuracy of 96% and 80.1%. The code and result of this work available in GitHub:https://github.com/haihabi/GeneticNAS.
@article{arxiv.1907.02871,
title = {Genetic Network Architecture Search},
author = {Hai Victor Habi and Gil Rafalovich},
journal= {arXiv preprint arXiv:1907.02871},
year = {2019}
}