Genetic Network Architecture Search

Abstract

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.

References in corpus (10)

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Neural Architecture Search with Reinforcement Learning
• Improved Regularization of Convolutional Neural Networks with Cutout
• DARTS: Differentiable Architecture Search
• DenseNet: Implementing Efficient ConvNet Descriptor Pyramids
• FractalNet: Ultra-Deep Neural Networks without Residuals
• Efficient Neural Architecture Search via Parameter Sharing
• Efficient Architecture Search by Network Transformation
• Shake-Shake regularization
• Hierarchical Representations for Efficient Architecture Search