Introduction to soft computing techniques: artificial neural networks, fuzzy logic and genetic algorithms

This chapter gives an overview of different ‘soft computing’(also known as ‘computational intelligence’) techniques that attempt to mimic imprecision and understanding of natural phenomena for algorithm development. It gives a detailed account of some of the popular evolutionary computing algorithms such as genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO) and artificial immune systems (AIS). The paradigm of fuzzy sets is introduced and two inferencing methods, the Mamdani model and the Takagi–Sugeno–Kang (TSK) model, are discussed. The genesis of brain modelling and its approximation so as to develop neural networks that can learn are also discussed. Two very popular computational intelligence techniques, support vector machines (SVMs) and rough sets, are introduced. The notions of hybridization that have aroused interest in developing new algorithms by using the better features of different techniques are mentioned. Each section contains applications of the respective technique in diverse domains.