Differential Evolution: From Theory to Practice: 1009 (Studies in Computational Intelligence) - Tapa dura

Acerca del autor

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 College (Tellippalai, Jaffna) and subsequently received the B.A degree, Postgraduate Certificate and M.A degree in Electrical and Information Engineering from the University of Cambridge, UK, in 1990, 1992 and 1994, respectively. He received an honorary doctorate (i.e. Doctor Honoris Causa) in 2020 from University of Maribor, Slovenia. After completing his Ph.D. research in 1995, he served as a pre-doctoral research assistant in the Department of Electrical Engineering, University of Sydney in 1995–96 and a lecturer in the Department of Computer Science and Electrical Engineering, University of Queensland in 1996–99. He was Editorial Board Member of the Evolutionary Computation Journal, MIT Press (2013–2018) and an associate editor of the IEEE Trans on Cybernetics (2012–2018). He is an associate editor of Applied Soft Computing (Elsevier, 2018- ), Neurocomputing (Elsevier, 2018- ), IEEE Trans on Evolutionary Computation (2005 - ), Information Sciences (Elsevier, 2009 - ), Pattern Recognition (Elsevier, 2001 - ) and IEEE Trans. on SMC: Systems (2020 - ). He is a founding co-editor-in-chief of Swarm and Evolutionary Computation (2010 - ), an SCI Indexed Elsevier Journal. His research interests include swarm and evolutionary algorithms, pattern recognition, forecasting, randomized neural networks, deep learning and applications of swarm, evolutionary and machine learning algorithms. His publications have been well cited (Google scholar Citations: ~45k). He was selected as one of the highly cited researchers by Thomson Reuters every year from 2015 to 2020 in computer science. He is ranked worldwide 300-400 among all Computer Science and Electronics Researchers (also include some Control and Communication Engineering researchers) with public Google Scholar profiles.

De la contraportada

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.