Constrained evolutionary piecemeal training to design convolutional neural networks

Neural Architecture Search (NAS), which automates the discovery of efficient neural networks, has demonstrated substantial potential in achieving state of the art performance in a variety of domains such as image classification and language understanding. In most NAS techniques, training of a neural network is considered a separate task or a performance estimation strategy to perform the architecture search. We demonstrate that network architecture and its coefficients can be learned together by unifying concepts of evolutionary search within a population based traditional training process. The consolidation is realised by cleaving the training process into pieces and then put back together in combination with evolution based architecture search operators. We show the competence and versatility of this concept by using datasets from two different domains, CIFAR-10 for image classification and PAMAP2 for human activity recognition. The search is constrained using minimum and maximum bounds on architecture parameters to restrict the size of neural network from becoming too large. Beginning the search from random untrained models, it achieves a fully trained model with a competent architecture, reaching an accuracy of 92.5% and 94.36% on CIFAR-10 and PAMAP2 respectively.