Springer Differential Evolution: From Theory to Practice: 1009 (Studies in Computational Intelligence, 1009)

Product Description This book addresses and disseminates state-of-the-art research and development of differential evolution (DE) and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques. Differential evolution is a population-based meta-heuristic technique for global optimization capable of handling non-differentiable, non-linear and multi-modal objective functions. Many advances have been made recently in differential evolution, from theory to applications. This book comprises contributions which include theoretical developments in DE, performance comparisons of DE, hybrid DE approaches, parallel and distributed DE for multi-objective optimization, software implementations, and real-world applications. The book is useful for researchers, practitioners, and students in disciplines such as optimization, heuristics, operations research and natural computing. From the Back Cover This book addresses and disseminates state-of-the-art research and development of differential evolution (DE) and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques. Differential evolution is a population-based meta-heuristic technique for global optimization capable of handling non-differentiable, non-linear and multi-modal objective functions. Many advances have been made recently in differential evolution, from theory to applications. This book comprises contributions which include theoretical developments in DE, performance comparisons of DE, hybrid DE approaches, parallel and distributed DE for multi-objective optimization, software implementations, and real-world applications. The book is useful for researchers, practitioners, and students in disciplines such as optimization, heuristics, operations research and natural computing. About the Author B Vinoth Kumar received the B.E. degree in Electronics and Communication Engineering from the Periyar University, India, in 2003, and the ME and Ph.D. degrees in Computer Science and Engineering from the Anna University, India, in 2009 and 2016, respectively. He is Associate Professor with 17 years of experience at PSG College of Technology, India. His current research interests include computational intelligence, memetic algorithms, and image processing. He has established an Artificial Intelligence Research (AIR) Laboratory at PSG College of Technology. He is Life Member of the Institution of Engineers, India (IEI), International Association of Engineers (IAENG) and Indian Society of Systems for Science and Engineering (ISSE). He is the author of more than 30 papers in refereed journals and international conferences. He has edited four books with reputed publishers such as Springer and CRC Press. He serves as Guest Editor/Reviewer of many journals with leading publishers such as Springer, Inderscience and De Gruyter. Diego Oliva received the B.S. degree in Electronics and Computer Engineering from the Industrial Technical Education Center (CETI) of Guadalajara, Mexico, in 2007, the M.Sc. degree in Electronic Engineering and Computer Sciences from the University of Guadalajara, Mexico, in 2010. He obtained the Ph.D. in Informatics in 2015 from the Universidad Complutense de Madrid. Currently, he is Associate Professor at the University of Guadalajara in Mexico. In 2017, he has been visiting professor at the Tomsk Polytechnic University in Russia. He has the distinction of National Researcher Rank 2 by the Mexican Council of Science and Technology. Since 2017, he is a member of the IEEE. He is a co-author of more than 100 papers in international journals and 5 books. He is part of the editorial board of IEEE Access, Plos One, Mathematical Problems in Engineering and IEEE Latin America Transactions. His research interest includes evolutionary and swarm algorithms, hybridization of evolutionary and swarm algorithms and computational intelligence. P N Suganthan finished schooling at Union Colle