Fundamentals of Soft Computing

Soft computing is a vital tool used for performing several computing operations. It uses one or more computational models or techniques to generate optimum outcomes. As researchers understood that soft computing can deal with imprecision, partial truth, and uncertainty, its applications are varied, ranging from day‐to‐day applications to various applications related to science and engineering. Some of the dominant characteristics of soft computing are: human expertise, biologically inspired computational models, optimization techniques, fault tolerant, goal‐driven, model‐free learning, and applicable to real‐world problems. The three principal components of soft computing include fuzzy logic‐based computing, neurocomputing, and genetic algorithms. These three components form the core of soft computing. There are a few other components of soft computing often used for problem solving, such as machine learning, probabilistic reasoning, evolutionary reasoning, and chaos theory.