  The computational models for geophysical flows are computationally very
expensive to employ in multi-query tasks such as data assimilation, uncertainty
quantification, and hence surrogate models sought to alleviate the
computational burden associated with these full order models. Researchers have
started applying machine learning algorithms, particularly neural networks, to
build data-driven surrogate models for geophysical flows. The performance of
the neural network highly relies upon its architecture design and selection of
other hyperparameters. These neural networks are usually manually designed
through trial and error to maximize their performance. This often requires
domain knowledge of the neural network as well as the problems of interest.
This limitation can be addressed by using an evolutionary algorithm to
automatically design architecture and select optimal hyperparameters of the
neural network. In this paper, we apply the genetic algorithm to effectively
design the long short-term memory (LSTM) neural network to build the
non-intrusive reduced order model of the sea-surface temperature field.
