Recent trends in nature inspired computation with applications to deep learning

Abstract

Nature-inspired computations are commonly recognized optimization techniques that provide optimal solutions to a wide spectrum of computational problems. This paper presents a brief overview of current topics in the field of nature-inspired computation along with their most recent applications in deep learning to identify open challenges concerning the most relevant areas. In addition, we highlight some recent hybridization methods of nature-inspired computation used to optimize the hyper-parameters and architectures of a deep learning framework. Future research as well as prospective deep learning issues are also presented. © 2020 IEEE.