Description
Revival: Genetic Algorithms for Pattern Recognition (1986)
CRC Press Revivals Series
Authors: Pal Sankar K., Wang Paul P.
Language: English
Subjects for Revival: Genetic Algorithms for Pattern Recognition (1986):
Keywords
Standard Artificial Neural Network; Fuzzy logic systems; Noisy Objective Function; Genetic Algorithms; Fuzzy Decision Trees; Intelligent recognition; Multimodal Function Optimization; Neural networks; GA; Pattern Recognition; Training Sample Points; Susmita De; Walsh Transform; Ashish Ghosh; Crossover Operator; Michael D; Vose; Membership Function; Alden H; Wright; Delta Coding; Lalit M; Patnaik; Hybrid Genetic Algorithms; Srinivas Mandavilli; Fuzzy Sets; Keith Mathias; Fuzzy Rule; Darrell Whitley; Schema Theorem; Anthony Kusuma; Initial Random Population; Christof Stork; Bit String; Benjamin W; Wah; Fitness Value; Arthur Ieumwananonthachai; Random Heuristic Search; Yong-Cheng Li; Bayes Classifier; Chivukula A; Murthy; Hidden Units; Sanghamitra Bandyopadhyay; Genetic Search; Hove Hugo Van; Vertex Cover Problem; Verschoren Alain; Fuzzy Controller; P; Buckles Bill; SGA; E; Petry Frederick; Recognition Tree; Prabhu Devaraya; Lybanon Matthew; G; Romaniuk Steve; Vincent Charles Gaudet; Hisao Ishibuchi; Tadahiko Murata; Hideo Tanaka; Cezary Z; Janikow; Cooper Mark G; Vidal Jacques J
Publication date: 01-2019
· 15.6x23.4 cm · Paperback
Publication date: 10-2017
· 15.6x23.4 cm · Hardback
Description
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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.