Genetic Algorithms for Pattern Recognition
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A01=Paul P. Wang
A01=Sankar K. Pal
Alden H. Wright
Anthony Kusuma
Arthur Ieumwananonthachai
Ashish Ghosh
Author_Paul P. Wang
Author_Sankar K. Pal
Bayes Classifier
Benjamin W. Wah
Bit String
Category=UB
Cezary Z. Janikow
Chivukula A. Murthy
Christof Stork
Cooper Mark G.
Crossover Operator
Darrell Whitley
Delta Coding
E. Petry Frederick
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eq_computing
eq_isMigrated=1
eq_isMigrated=2
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evolutionary computation
evolutionary pattern classification systems
fitness function optimization
Fitness Value
Fuzzy Controller
Fuzzy Decision Trees
fuzzy logic systems
Fuzzy Rule
Fuzzy Sets
G. Romaniuk Steve
GA
Genetic Algorithms
Genetic Search
heuristic learning algorithms
Hidden Units
Hideo Tanaka
Hisao Ishibuchi
Hove Hugo Van
Hybrid Genetic Algorithms
Initial Random Population
Intelligent recognition
Keith Mathias
Lalit M. Patnaik
Lybanon Matthew
Membership Function
Michael D. Vose
Multimodal Function Optimization
neural network classification
Neural networks
Noisy Objective Function
P. Buckles Bill
Pattern Recognition
Prabhu Devaraya
Random Heuristic Search
Recognition Tree
Sanghamitra Bandyopadhyay
satellite image analysis
Schema Theorem
SGA
Srinivas Mandavilli
Standard Artificial Neural Network
Susmita De
Tadahiko Murata
Training Sample Points
Verschoren Alain
Vertex Cover Problem
Vidal Jacques J.
Vincent Charles Gaudet
Walsh Transform
Yong-Cheng Li
Product details
- ISBN 9781138558885
- Weight: 453g
- Dimensions: 156 x 234mm
- Publication Date: 25 Jan 2019
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Paperback
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Solving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved. Genetic Algorithms for Pattern Recognition covers a broad range of applications in science and technology, describing the integration of genetic algorithms in pattern recognition and machine learning problems to build intelligent recognition systems.
The articles, written by leading experts from around the world, accomplish several objectives: they provide insight into the theory of genetic algorithms; they develop pattern recognition theory in light of genetic algorithms; and they illustrate applications in artificial neural networks and fuzzy logic. The cross-sectional view of current research presented in Genetic Algorithms for Pattern Recognition makes it a unique text, ideal for graduate students and researchers.
Sankar Kumar Pal is a Distinguished Scientist and former Director of the Indian Statistical Institute, Kolkata, India. He is a computer scientist with an international reputation on fuzzy neural network, soft computing, and machine intelligence. He founded the Machine Intelligence Unit in 1993, and the Center for Soft Computing Research: A National Facility in 2004, both at the ISI. He is the founder President of the Indian National Academy of Engineering, Kolkata Chapter
