  Deep neural networks have recently drawn considerable attention to build and
evaluate artificial learning models for perceptual tasks. Here, we present a
study on the performance of the deep learning models to deal with global
optimization problems. The proposed approach adopts the idea of the neural
architecture search (NAS) to generate efficient neural networks for solving the
problem at hand. The space of network architectures is represented using a
directed acyclic graph and the goal is to find the best architecture to
optimize the objective function for a new, previously unknown task. Different
from proposing very large networks with GPU computational burden and long
training time, we focus on searching for lightweight implementations to find
the best architecture. The performance of NAS is first analyzed through
empirical experiments on CEC 2017 benchmark suite. Thereafter, it is applied to
a set of protein structure prediction (PSP) problems. The experiments reveal
that the generated learning models can achieve competitive results when
compared to hand-designed algorithms; given enough computational budget
