- Soft-computing addressed to the community of computational physicists and chemists, for the first time.
- Equal emphasis given to the theoretical developments and practical applications of a host of soft computing methods like the GA, Genetic computing Swarm intelligence, artificial neural networks, evolutionary computing and a number of hybrid intelligent systems in physical and chemical sciences
- Addresses the problem of interfacing soft computing with electronic structure calculations, reaction path modeling, spin glass problems, designing molecules with targeted properties, provides solutions and encourages the researchers to develop new algorithms.
- Written in a 'self-taught' style avoiding jargon and providing enough examples to make the reader confident.
- Futuristic in outlook, the book explores the possible synergy between soft computing and quantum computing paradigms and the shape of things to come.
This book can be regarded as 'Soft computing for physicists and chemists self-taught'. It prepares the readers with a solid background of soft computing and how to adapt soft computing techniques to problem solving in physical and chemical research. Soft computing methods have been little explored by researchers in physical and chemical sciences primarily because of the absence of books that bridge the gap between the traditional computing paradigm pursued by researchers in science and the new soft computing paradigm that has emerged in computer science. This book is the interface between these primary sources and researchers in physics and chemistry.
Table of Contents
A New Computing Paradigm. Genetic Algorithms. Evolutionary Computing. Random Mutation Hill Climbing and Simulated Annealing Methods. Swarm Intelligence. Application of Soft-computing in Physics. Soft Computing in Chemistry. Artificial Neural Networks. Fuzzy Systems. Quantum and Soft-Computing.