Classification and Learning Using Genetic Algorithms

Classification and Learning Using Genetic Algorithms

Applications in Bioinformatics and Web Intelligence

Bandyopadhyay, SanghamitraPal, Sankar Kumar

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Description

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-a-vis several widely used classifiers, including neural networks.

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Details

ISBN/GTIN978-3-642-08054-8

Product TypeBook

BindingPaperback

PublisherSpringer

Publishing date23/11/2010

EditionSoftcover reprint of hardcover

SeriesNatural Computing Series

Pages311 pages

LanguageEnglish

SizeWidth 155 mm, Height 235 mm

Weight510 g

IllustrationsXVI, 311 p. 87 illus., schwarz-weiss Illustrationen

Article no.2276590

CatalogsBuchzentrum

Data source no.10686569

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Series

Advances in Computational Intelligence

Advances in Evolutionary Computing

Membrane Computing

Quantum Computing

Computation in Living Cells

Evolvable Components

Modelling in Molecular Biology

Designing Evolutionary Algorithms for Dynamic Environments

Computation in Cells and Tissues

Spatially Structured Evolutionary Algorithms

Bee-Inspired Protocol Engineering

Biologically Inspired Algorithms for Financial Modelling

Classification and Learning Using Genetic Algorithms

Swarm Intelligence

Experimental Research in Evolutionary Computation

Algorithmic Aspects of Bioinformatics

Bee-Inspired Protocol Engineering

Designing Evolutionary Algorithms for Dynamic Environments

The Theory of Evolution Strategies

Theoretical and Experimental DNA Computation

Theoretical Aspects of Evolutionary Computing

Nanotechnology: Science and Computation

Computation in Cells and Tissues

Automating the Design of Data Mining Algorithms

Sensitivity Analysis for Neural Networks

Algorithmic Bioprocesses

Modelling in Molecular Biology

Advances in Evolutionary Computing

Evolvable Components

Self-organising Software

Theory and Principled Methods for the Design of Metaheuristics

Differential Evolution

The Art of Artificial Evolution

Natural Computing Algorithms

Discrete and Topological Models in Molecular Biology

Contemporary Evolution Strategies

Self-aware Computing Systems

Multimodal Optimization by Means of Evolutionary Algorithms

Theory of Evolutionary Computation

The Science of Soft Robots

Author

Bandyopadhyay, SanghamitraPal, Sankar Kumar

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.