Applied and Computational Mathematics 2017; 6(2): 93-110 http://www.sciencepublishinggroup.com/j/acm doi: 10.11648/j.acm.20170602.15 ISSN: 2328-5605 (Print); ISSN: 2328-5613 (Online) Review Article A Holistic Review of Soft Computing Techniques Philip O. Omolaye * , Joseph M. Mom, Gabriel A. Igwue Department of Electrical and Electronics Engineering, University of Agriculture, Makurdi, Nigeria Email address: philomolaye@gmail.com (P. O. Omolaye), joe.mom@uam.edu.ng (J. M. Mom), gaigwue@yahoo.com (G. A. Igwue) * Corresponding author To cite this article: Philip O. Omolaye, Joseph M. Mom, Gabriel A. Igwue. A Holistic Review of Soft Computing Techniques. Applied and Computational Mathematics. Vol. 6, No. 2, 2017, pp. 93-110. doi: 10.11648/j.acm.20170602.15 Received: February 15, 2017; Accepted: March 17, 2017; Published: April 10, 2017 Abstract: Due to notable technological convergence that brought about exponential growth in computer world, Soft Computing (SC) has played a vital role with automation capability features to new levels of complex applications. In this research paper, the authors reviewed journals related to the subject matter with the aim of striking a convincing balance between a system that is capable of tolerance to uncertainty, imprecision, approximate reasoning and partial truth to achieve tractability, robustness, economy of communication, high machine intelligence quotient (MIQ), low cost solution and better rapport with reality to conventional techniques. This paper gives an insight on four major consortiums of SC that sprang from the concept of cybernetics, explores and reviews the different techniques, methodologies; application areas and algorithms are formulated to give an idea on how these computing techniques are applied to create intelligent agents to solve a variety of problems. The mechanisms highlighted can serve as an inspiration platform and awareness to new and old researchers that are not or fully grounded in this unique area of research and to create avenue in order to fully embrace the techniques in research communities. Keywords: Machine Intelligence, Soft Computing, Hard Computing, Hybrid Computing, Neural Network, Fuzzy Logic, Evolutionary Computation, Ant Colony Algorithm 1. Introduction Artificial Intelligence (AI) is a broad field of study with different meanings to researchers depending on individual perspectives. AI techniques are known to be applied to solve complex and ill-defined problems in which soft computing, computational intelligence and granular computing form some of the major offshoot. McCarthy John [143], based on his perspective, sees AI as “computational intelligence” while Zadeh in [234], [235] claimed that computational intelligence is actually Soft Computing (SC). Irrespective of the meaning attached to it, AI or SC has to do with human intelligence which requires complex and advanced reasoning processes and knowledge in executing diverse applications such as control, forecasting, robotics, pattern recognition, medicine, and optimization, signal processing and industrial applications [1], [29], [38], [224].In this age, there are several techniques used or applied in solving real world problems such as Hard computing (HC), Soft computing (SC) and Hybrid computing (HyC) with striking or peculiar characteristics and features [47]. HC is also known as the conventional methods that are based on mathematical techniques such as crisp systems, binary logic, numerical analysis and finite element analysis. However, the HC has the characteristics of precision and categorization although imprecision and uncertainties are undesirable properties. The HC is easy to model mathematically but requires a precisely stated analytical model, strictly sequential and produces precise solutions in which stability is highly predictable, deterministic in nature and often requires a lot of computation time [111], [168]. The SC acts as an umbrella or suite of computing techniques in which each of the techniques contributes a dissimilar methodology to address a general problem in its domain in such a way that the principal component methodologies complement each other rather than being competitive in nature. SC comprises wide range of terms, encompassing several techniques but for the purpose of this paper, four (4) out of the numerous techniques are treated with a bias treatment of ant colony optimization (ACO). The four (4) SC consortiums are fuzzy system (FS), artificial neural