  Neural networks have been successfully employed in various domains such as
classification, regression and clustering, etc. Generally, the back propagation
(BP) based iterative approaches are used to train the neural networks, however,
it results in the issues of local minima, sensitivity to learning rate and slow
convergence. To overcome these issues, randomization based neural networks such
as random vector functional link (RVFL) network have been proposed. RVFL model
has several characteristics such as fast training speed, direct links, simple
architecture, and universal approximation capability, that make it a viable
randomized neural network. This article presents the first comprehensive review
of the evolution of RVFL model, which can serve as the extensive summary for
the beginners as well as practitioners. We discuss the shallow RVFLs, ensemble
RVFLs, deep RVFLs and ensemble deep RVFL models. The variations, improvements
and applications of RVFL models are discussed in detail. Moreover, we discuss
the different hyperparameter optimization techniques followed in the literature
to improve the generalization performance of the RVFL model. Finally, we give
potential future research directions/opportunities that can inspire the
researchers to improve the RVFL's architecture and learning algorithm further.
