NEMO Neuro Evolution with Multiobjective Optimization of Deep Neural Network for Speed and Accuracy
This paper proposes automatic machine learning approach (AutoML) of deep neural networks (DNNs) using multi objective evolutionary algorithms (MOEAs) for the accuracy and run time speed simultaneously. Traditional methods for optimizing DNNs have used Bayesian reasoning or reinforcement learning to improve the performance. Recently, evolutionary approaches for high accuracy has been adopted using a lot of GPUs. However, real world applications require rapid inference to deploy on embedded devices while maintaining high accuracy. To consider both accuracy and speed at the same time, we propose Neuro Evolution with Multiobjective Optimization (NEMO) algorithm. Experimental results show that proposed NEMO finds faster and more accurate DNNs than hand crafted DNN architectures. The proposed technique is verified for the image classification problems such as MNIST, CIFAR 10 and human status recognition.