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This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. The book is unique in the sense of describing how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries, and it demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks. It provides a balanced mixture of theories, algorithms and applications, and in particular results from the bioinformatics and Web intelligence domains.
This book will be useful to graduate students and researchers in computer science, electrical engineering, systems science, and information technology, both as a text and reference book. Researchers and practitioners in industry working in system design, control, pattern recognition, data mining, soft computing, bioinformatics and Web intelligence will also benefit.
Sommario
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.
Info autore
Prof. Sanghamitra Bandyopadhyay has many years of experience in the development of soft computing techniques. Among other awards and positions, she has received senior researcher Humboldt Fellowships, and she is a regular visitor to the DKFZ (German Cancer Research Centre) and to European and North American universities, collaborating in multidisciplinary teams on applications in the areas of computational biology and bioinformatics. Among other awards Prof. Bandyopadhyay received the prestigious Shanti Swarup Bhatnagar Prize in Engineering Sciences in 2010, she is a Fellow of the National Academy of Sciences of India and she is a Fellow of the Indian National Academy of Engineering. Dr. Sriparna Saha is an assistant professor in the Indian Institute of Technology Patna. Among her positions and awards, she was a postdoctoral researcher in Trento and in Heidelberg, and she received the Google India Women in Engineering Award in 2008. Her research interests include multiobjective optimization, evolutionary computation, clustering, and pattern recognition.
Sankar K. Pal, PhD, is a Distinguished Scientist and founding head of the Machine Intelligence Unit at the Indian Statistical Institute, Calcutta. Professor Pal holds several PhDs and is a Fellow of the IEEE and IAPR.
Riassunto
This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It provides a balanced mixture of theories, algorithms and applications, and in particular results from the bioinformatics and Web intelligence domains. The book will be useful to graduate students and researchers in computer science, electrical engineering, systems science, and information technology, both as a text and reference book. Researchers and practitioners in industry working in system design, control, pattern recognition, data mining, soft computing, bioinformatics and Web intelligence will also benefit.
Testo aggiuntivo
"This book tries to balance the mixture of theories, algorithms, and applications and is a good reference for people who want to solve a complex optimization problem for their field. ... Overall, this book is well organized and well written. There is no doubt that this is another good pattern recognition reference to have on one's bookshelf." (Zheng Liu, IAPR Newsletter 30(4), October 2008)
Relazione
"This book tries to balance the mixture of theories, algorithms, and applications and is a good reference for people who want to solve a complex optimization problem for their field. ... Overall, this book is well organized and well written. There is no doubt that this is another good pattern recognition reference to have on one's bookshelf." (Zheng Liu, IAPR Newsletter 30(4), October 2008)
