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1. Introduction
The Feedforward Neural Network (FNN) has gained much attention from researchers in the last few decades (Abdel-Hamid et al., 2014; Babaee et al., 2018; Chen et al., 2018; Chung et al., 2017; Deng et al., 2019; Dong et al., 2016; Ijjina and Chalavadi, 2016; Kastrati et al., 2019; Kummong and Supratid, 2016; Mohamed Shakeel et al., 2019; Nasir et al., 2019; Teo et al., 2015; Yin and Liu, 2018; Zaghloul et al., 2009) because of its ability to extract useful patterns and make a more informed decision from high dimensional data (Kumar et al., 1995; Tkáč and Verner, 2016; Tu, 1996). With modern information technology advancement, the challenging issue of high dimensional, non-linear, noisy and unbalanced data are continuously growing and varying at a rapid rate so that it demands efficient learning algorithms and optimization techniques (Shen, Choi and Chan, 2019; Shen and Chan, 2017). The data may become a costly resource if not analyzed properly in the process of business intelligence. Machine learning is gaining significant interest in facilitating business intelligence in the process of data gathering, analyses and extracting knowledge to help users in making better informed decisions (Bottani et al., 2019; Hayashi et al., 2010; Kim et al., 2019; Lam et al., 2014; Li et al., 2018; Mori et al., 2012; Wang et al., 2005; Wong et al., 2018). Efforts are being made to overcome the challenges by building optimal machine learning FNNs that may extract useful patterns from the data and generate information in real-time for better-informed decision making. Extensive knowledge and theoretical information are required to build FNNs having the characteristics of better generalization performance and learning speed. The generalization performance and learning speed are the two criteria that play an essential role in deciding on the use of learning algorithms and optimization techniques to build optimal FNNs. Depending upon the application and data structure, the user might prefer either better generalization performance or faster learning speed, or a combination of both. Some of the drawbacks that may affect the generalization performance and learning speed of FNNs include local minima, saddle points, plateau surfaces, hyperparameters adjustment, trial and...