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Applied Genetic Algorithm and Its Variants: Case Studies and New Developments (Springer Tracts in Nature-Inspired Computing) - Hardcover

 
9789819934270: Applied Genetic Algorithm and Its Variants: Case Studies and New Developments (Springer Tracts in Nature-Inspired Computing)

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This book provides fundamental concepts related to various types of genetic algorithms and practical applications in various domains such as medical imaging, manufacturing, and engineering design. The book discusses genetic algorithms which are used to solve a variety of optimization problems. The genetic algorithms are demonstrated to offer reliable search in complex spaces. The book presents high-quality research work by academics and researchers which is useful for young researchers and students.

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About the Author

Nilanjan Dey  is Associate Professor, Department of Computer Science and Engineering, Techno International New Town, Kolkata, India. He is Visiting Fellow of the University of Reading, UK. He was Honorary Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012-2015). He was awarded his Ph.D. from Jadavpur University in 2015. He has authored/edited more than 70 books with Elsevier, CRC Press, and Springer and published more than 300 papers. He is Editor-in-Chief of International Journal of Ambient Computing and Intelligence, IGI Global, and Associated Editor of  International Journal of Information Technology, Springer. He is Series Co-editor of Springer Tracts in Nature-Inspired Computing, Springer, Series Co-editor of Advances in Ubiquitous Sensing Applications for Healthcare, Elsevier, and Series Editor of Computational Intelligence in Engineering Problem Solving and Intelligent Signal processing and data analysis, CRC. He is Fellow of IETE and Senior Member of IEEE.


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This book provides fundamental concepts related to various types of genetic algorithms and practical applications in various domains such as medical imaging, manufacturing, and engineering design. The book discusses genetic algorithms which are used to solve a variety of optimization problems. The genetic algorithms are demonstrated to offer reliable search in complex spaces. The book presents high-quality research work by academics and researchers which is useful for young researchers and students.

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