This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks.

First book to provide a unified framework that describes how genetic learning can be used to design pattern recognition systems Includes supplementary material: sn.pub/extras

Autorentext

Dr. Anirban Mukhopadhyay is currently a Professor of the Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal. He obtained his Ph.D. in Computer Science and Engineering from Jadavpur University, Kolkata, India in 2009. He received Erasmus Mundus fellowship in 2009 to carry out post-doctoral research at University of Heidelberg and DKFZ, Heidelberg, Germany during 2009-10. Dr. Mukhopadhyay also worked as visiting professor/scientist at University of Nice Sophia-Antipolis, France, University of Goettingen, Germany (with DAAD scholarship), Colorado State University, USA (with Fulbright-Nehru Fellowship), University of Greifswald, Germany, and University of Lodz, Poland (with Erasmus+ fellowship). He received IEI Young Engineers Award (2013-14) in Computer Engineering Discipline, and INAE Young Engineer Award (2014). He has coauthored two books and about 200 research papers in various International Journals and Conferences. Dr. Mukhopadhyaydelivered invited lectures and served in the Technical Program Committees in many national and international conferences in India and abroad. He is a senior member of IEEE and ACM, and a Fellow of West Bengal Academy of Science and Technology. Dr. Mukhopadhyay received the prestigious Sikhsha Ratna award from Govt. of West Bengal in 2020. He has served as a secretary of IEEE Computational intelligence Society, Kolkata Chapter and currently acts as the Vice-Chair of its Executive Committee. He has successfully guided eleven Ph.D. scholars. His research interests include soft and evolutionary computing, data mining and machine learning, multiobjective optimization, bioinformatics and crowdsourcing. Dr. Sumanta Ray is currently an associate professor in the Department of Computer Science and Engineering at Ghani Khan Choudhury Institute of Engineering & Technology (GKCIET), Malda, India. He earned his PhD in Computer Science and Engineering from Jadavpur University in 2017. Dr. Ray received an ERCIM (European Research Consortium for Informatics and Mathematics) grant to pursue postdoctoral research at CENTRUM WISKUNDE and INFORMATICA (CWI), the Netherlands, from 2019 to 2020. He was a recipient of the DST Inspire Fellowship from the Government of India and also received the Senior Research Fellowship from CSIR (Council of Scientific and Industrial Research), MHRD, Government of India. Previously, Dr. Ray served as a Junior Professor at Universität Bielefeld, Bielefeld, Germany, and as an Assistant Professor at the Department of Computer Science and Engineering, Aliah University, Kolkata. He is currently on lien from Aliah University. Dr. Ray has co-authored more than 40 research papers in various journals and conferences. His research interests include bioinformatics, soft and evolutionary computing, multiobjective optimization, deep learning, and pattern recognition. Dr. Ujjwal Maulik is a professor in the Department of Computer Science and Engineering, Jadavpur University, Kolkata, India since 2004. He was also the former head of the same Department. He also held the position of the principal in charge and the head of the Department of Computer Science and Engineering, Kalyani Government Engineering College, Kalyani, India. Dr. Maulik has worked in many universities and research laboratories around the world as visiting professor/scientist including Los Alamos National Laboratory, USA; University of New South Wales, Australia; University of Texas at Arlington, USA; University of Maryland at Baltimore County, USA; Fraunhofer Institute for Autonomous Intelligent Systems, St. Augustin, Germany; Tsinghua University, China; Sapienza University, Rome, Italy; University of Heidelberg, Germany; German Cancer Research Center (DKFZ), Germany; Grenoble INP, France; University of Warsaw; University of Padova, Italy; Corvinus University, Budapest; University of Ljubljana, Slovenia; InternationalCenter for Theoretical Physics (ICTP),

Inhalt
Genetic Algorithms.- Supervised Classification Using Genetic Algorithms.- Theoretical Analysis of the GA-classifier.- Variable String Lengths in GA-classifier.- Chromosome Differentiation in VGA-classifier.- Multiobjective VGA-classifier and Quantitative Indices.- Genetic Algorithms in Clustering.- Genetic Learning in Bioinformatics.- Genetic Algorithms and Web Intelligence.