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040 _aOCoLC-P
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_cOCoLC-P
020 _a9780203713402
_q(electronic book)
020 _a0203713400
_q(electronic book)
020 _a9781351364492
_q(electronic book ;
_qelectronic book)
020 _a1351364499
020 _a9781351364485
020 _a1351364480
020 _a9781351364478
020 _a1351364472
020 _z1138105570
_q(print)
020 _z9781138105577
_q(print)
020 _z9781138558885
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024 7 _a10.1201/9780203713402
_2doi
035 _a(OCoLC)1014359346
035 _a(OCoLC-P)1014359346
050 4 _aQ327
_b.G46 2017
072 7 _aCOM012000
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072 7 _aCOM051240
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072 7 _aMAT003000
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072 7 _aCOM
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072 7 _aCOM
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072 7 _aMAT
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072 7 _aUB
_2bicssc
082 0 4 _a006.4
_223
245 0 0 _aGenetic algorithms for pattern recognition /
_cedited by Sankar K. Pal, Paul P. Wang.
250 _aFirst edition.
264 1 _aBoca Raton, FL :
_bCRC Press,
_c2017.
300 _a1 online resource (xx, 314 pages) :
_billustrations
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 0 _aCRC Press Revivals
520 2 _a"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."--Provided by publisher.
505 0 _aCover; Title Page; Copyright Page; Dedication; Contents; Preface; Editors; Contributors; 1 Fitness Evaluation in Genetic Algorithms with Ancestorsâ#x80;#x99; Influence; 1.1 Introduction; 1.2 Genetic Algorithms: Basic Principles and Features; 1.3 A New Fitness Evaluation Criterion; 1.3.1 Selection of Weighting Coefficients (α, β, γ); 1.3.2 The Schema Theorem and the Influence of Parents on the Offspring; 1.4 Implementation; 1.4.1 Selection of Genetic Parameters; 1.4.2 Various Schemes; 1.5 Analysis of Results; 1.6 Conclusions; 2 The Walsh Transform and the Theory of the Simple Genetic Algorithm.
505 8 _a2.1 Introduction2.2 Random Heuristic Search; 2.2.1 Notation; 2.2.2 Selection; 2.2.3 Mutation; 2.2.4 Crossover; 2.2.5 The Heuristic Function of the Simple Genetic Algorithm; 2.3 The Walsh Transform; 2.4 The Walsh Basis; 2.5 Invariance; 2.6 The Inverse GA; 2.7 Recombination Limits; 2.8 Conclusion; 3 Adaptation in Genetic Algorithms; 3.1 Introduction; 3.2 Exploitation vs. Exploration in Genetic Algorithms; 3.3 Why Adapt Control Parameters?; 3.4 Adaptive Probabilities of Crossover and Mutation; 3.4.1 Motivations; 3.4.2 Design of Adaptive p[sub(c)] and p[sub(m)].
505 8 _a3.4.3 Practical Considerations and Choice of Values for k[sub(1)], k[sub(2)], and k[sub(3)]3.5 Experiments and Results; 3.5.1 Performance Measures; 3.5.2 Functions for Optimization; 3.5.3 Experimental Results; 3.5.4 When Does the AGA Perform Well?; 3.5.5 Sensitivity of AGA to k[sub(1)] and k[sub(2)]; 3.6 Conclusions; 4 An Empirical Evaluation of Genetic Algorithms on Noisy Objective Functions; 4.1 Introduction; 4.2 Background; 4.3 Empirical Benchmarks; 4.3.1 Algorithm Descriptions; 4.4 Performance Comparisons Using Noisy Fitness Values to Approximate Optimality.
505 8 _a4.4.1 Empirical Results and Analysis4.5 Performance Comparisons Using True Fitness Values in Noisy Optimization Environments; 4.5.1 Empirical Results and Analysis; 4.6 Discussion of Empirical Tests; 4.7 An Application: Geophysical Static Corrections; 4.7.1 Problem Description; 4.7.2 Algorithm Descriptions; 4.7.3 Empirical Results and Analysis; 4.8 Conclusions; 5 Generalization of Heuristics Learned in Genetics-Based Learning; 5.1 Introduction; 5.1.1 Generation of Heuristics; 5.1.2 Testing of Heuristics and Evaluating Their Performance.
505 8 _a5.1.3 Generalization of Heuristics Learned to Unlearned Domains5.2 Performance Evaluation and Anomalies; 5.2.1 Example Applications; 5.2.2 Problem Subspace and Subdomain; 5.2.3 Anomalies in Performance Evaluation; 5.3 Generalization of Heuristic Methods Learned; 5.3.1 Probability of Win within a Subdomain; 5.3.2 Probability of Win across Subdomains; 5.3.3 Generalization Procedure; 5.4 Experimental Results; 5.4.1 Heuristics for Sequential Circuit Testing; 5.4.2 Heuristics for VLSI Placement and Routing; 5.4.3 Branch-and-Bound Search; 5.5 Conclusions.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aPattern perception.
_96089
650 0 _aMachine learning.
_91831
650 0 _aGenetic algorithms.
_93938
650 7 _aCOMPUTERS
_xGeneral.
_2bisacsh
_94629
650 7 _aCOMPUTERS / Computer Graphics / General
_2bisacsh
_912490
650 7 _aCOMPUTERS / Programming / Systems Analysis & Design
_2bisacsh
_912930
650 7 _aMATHEMATICS / Applied
_2bisacsh
_96859
700 1 _aPal, Sankar K.,
_eeditor.
_911439
700 1 _aWang, Paul P.,
_eeditor.
_918360
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/e/9781351364492
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/e/9780203713402
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9780203713402
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
938 _aTaylor & Francis
_bTAFR
_n9780203713402
942 _cEBK
999 _c71776
_d71776