This two-volume set gathers 25 peer-reviewed chapters on artificial intelligence algorithms and techniques for handling uncertainties. International contributors in information technology, electronics engineering, theoretical physics, and computer science present metaheuristic optimization (MO) methods inspired by various phenomena in nature and man-made activities, such as fuzzy logic, artificial neural networks, particle swarm optimization, glow worm swarm optimization, bee algorithms, ant colony algorithms, and more.
– ProtoView Book Abstracts (formerly Book News, Inc.)
Effectively organized, with the 25 chapters providing coverage at the right level of width and depth, providing the reader with an overview of the most important ideas and the sufficient amount of detail to drive further, focused, study. [...] Recommended. The book is balanced with valuable contributions, and adds value to the scientific literature on the subject.
– Dr. Ugo Fiore, Parthenope University, Italy
...Gives a profound description of a very important area of research and will be useful for many specialists in their practical activities. This book can be useful for students, engineers, scientists, economists, financiers, managers, researchers, decision makers and other specialists who deal with the necessity to analyze and make decisions in different areas of reality.
– Professor Nikolai Voropai, Director of the Melentiev Energy Systems Institute, Irkutsk, Russia
Recommended for its extremely wide scope covering huge areas of modern cutting-edge research for dealing with all kinds of uncertainties that one can imagine in real life problems. Major parts of the book are written in a way that's accessible for undergraduate students with the basic knowledge in applied mathematics, statistics and operational research.
– Professor Vassili Kolokoltsov, Department of Statistics, University of Warwick