  Early advancements in convolutional neural networks (CNNs) architectures are
primarily driven by human expertise and by elaborate design processes.
Recently, neural architecture search was proposed with the aim of automating
the network design process and generating task-dependent architectures. While
existing approaches have achieved competitive performance in image
classification, they are not well suited to problems where the computational
budget is limited for two reasons: (1) the obtained architectures are either
solely optimized for classification performance, or only for one deployment
scenario; (2) the search process requires vast computational resources in most
approaches. To overcome these limitations, we propose an evolutionary algorithm
for searching neural architectures under multiple objectives, such as
classification performance and floating-point operations (FLOPs). The proposed
method addresses the first shortcoming by populating a set of architectures to
approximate the entire Pareto frontier through genetic operations that
recombine and modify architectural components progressively. Our approach
improves computational efficiency by carefully down-scaling the architectures
during the search as well as reinforcing the patterns commonly shared among
past successful architectures through Bayesian model learning. The integration
of these two main contributions allows an efficient design of architectures
that are competitive and in most cases outperform both manually and
automatically designed architectures on benchmark image classification
datasets: CIFAR, ImageNet, and human chest X-ray. The flexibility provided from
simultaneously obtaining multiple architecture choices for different compute
requirements further differentiates our approach from other methods in the
literature. Code is available at https://github.com/mikelzc1990/nsganetv1
