Soft computing is that part of computational science that is applied to problems characterized by uncertainty and imprecision. The techniques of soft computing include fuzzy sets, numbers, and logic; neural networks; and genetic programming and algorithms. This volume contains ten papers of theory and applications in engineering and applied science.

The application areas selected are: medical (Parkinson’s disease), multiobjective programming, Stackelberg non-cooperative games, activation energies for reaction rate constants in biomass pyrolysis, air quality forecasting, electric discharge machining, and system reliability prediction. The techniques applied include artificial neural networks (ANNs), genetic programming and algorithms, and fuzzy numbers and sets. Intuitionistic fuzzy numbers and sets are prominent in three of the ten papers, including an extensive theoretical presentation of “generalized semielliptic intuitionistic fuzzy numbers and their application to multicriteria decision making” (Dutta and Saikia).

When ANNs and genetic programming techniques are used, they are both used comparatively in two of the papers--air quality forecasting (Tikhe, Khare, and Londhe) and optimizing electric discharge machining (Bose and Pain). The paper on Parkinson’s disease (Rai and Meshram) used only neural networks to detect changes in patient gaits.

The paper on activation energies (Dhaundiyal and Singh) differs from the other nine in that it does not use neural networks, genetic programming, or fuzzy numbers. It develops the mathematics to generate several classical statistical distributions of activation energies used to calculate reaction rate constants in biomass pyrolysis--Lindley, Weibull, Fréchet, Gaussian, and Rayleigh--and apply them to model experiments.

The most appropriate readers of this book are people already familiar with soft computing application areas and theory.