  This study presents a novel training algorithm depending upon the recently
proposed Fitness Dependent Optimizer (FDO). The stability of this algorithm has
been verified and performance-proofed in both the exploration and exploitation
stages using some standard measurements. This influenced our target to gauge
the performance of the algorithm in training multilayer perceptron neural
networks (MLP). This study combines FDO with MLP (codename FDO-MLP) for
optimizing weights and biases to predict outcomes of students. This study can
improve the learning system in terms of the educational background of students
besides increasing their achievements. The experimental results of this
approach are affirmed by comparing with the Back-Propagation algorithm (BP) and
some evolutionary models such as FDO with cascade MLP (FDO-CMLP), Grey Wolf
Optimizer (GWO) combined with MLP (GWO-MLP), modified GWO combined with MLP
(MGWO-MLP), GWO with cascade MLP (GWO-CMLP), and modified GWO with cascade MLP
(MGWO-CMLP). The qualitative and quantitative results prove that the proposed
approach using FDO as a trainer can outperform the other approaches using
different trainers on the dataset in terms of convergence speed and local
optima avoidance. The proposed FDO-MLP approach classifies with a rate of 0.97.
