Nature-Inspired Computing (Physics and Chemistry-Based Algorithms)

Nature-Inspired Computing: Physics and Chemistry-Based Algorithms provides a comprehensive introduction to the methodologies and algorithms in nature-inspired computing, with an emphasis on applications to real-life engineering problems.

The research interest for Nature-inspired Computing has grown considerably exploring different phenomena observed in nature and basic principles of physics, chemistry, and biology. The discipline has reached a mature stage and the field has been well-established. The inspiration of writing this book on Nature-inspired Computing in three volumes came from the book, Machine Learning - Neural Networks, Genetic Algorithms, and Fuzzy Systems, Hojjat Adeli and S.L. Hung (John Wiley and Sons, 1995) as the first treatise that presented the three main fields of computational intelligence in a single book. That led the authors first to write the book, Computational Intelligence – Synergies of Fuzzy Logic, Neural Networks, and Evolutionary Computing, Nazmul Siddique and Hojjat Adeli (John Wiley and Sons, 2013.) This endeavor is another attempt at investigation into various computational schemes inspired from nature, which are presented in this book with the development of a suitable framework and industrial applications.

Designed for senior undergraduates, postgraduates, research students, and professionals, the book is written at a comprehensible level for students who have some basic knowledge of calculus and differential equations, and some exposure to optimization theory. Due to the focus on search and optimization, the book is also appropriate for electrical, control, civil, industrial and manufacturing engineering, business, and economics students, as well as those in computer and information sciences.

The book can also serve as a reference for researchers and scientists in the fields of sytem science, natural computing, and optimization.