Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence

✍ Scribed by Sanghamitra Bandyopadhyay, Sankar K. Pal (auth.)

• Publisher: Springer Berlin Heidelberg
• Year: 2007
• Tongue: English
• Leaves: 319
• Series: Natural Computing Series
• Category: Library

✦ Synopsis

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-� -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.

✦ Table of Contents

Content:
Front Matter....Pages I-XV
Introduction....Pages 1-18
Genetic Algorithms....Pages 19-51
Supervised Classification Using Genetic Algorithms....Pages 53-80
Theoretical Analysis of the GA-classifier....Pages 81-107
Variable String Lengths in GA-classifier....Pages 109-137
Chromosome Differentiation in VGA-classifier....Pages 139-157
Multiobjective VGA-classifier and Quantitative Indices....Pages 159-180
Genetic Algorithms in Clustering....Pages 181-212
Genetic Learning in Bioinformatics....Pages 213-241
Genetic Algorithms and Web Intelligence....Pages 243-276
Back Matter....Pages 277-311