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Genetic algorithms: concepts and designs

Genetic algorithms: concepts and designs (Loan 3 times)

Material type
단행본
Personal Author
Man, K. F. (Kim F.) , 1951-.0 1. Tang, K. S 1967- Kwong, S 1959-
Title Statement
Genetic algorithms: concepts and designs / K.F. Man, K.S. Tang, and S. Kwong.
Publication, Distribution, etc
London ;   New York :   Springer ,   c1999.  
Physical Medium
xii, 344 p. : ill. ; 24 cm. + 1 computer disk (3 1/2 in.).
Series Statement
Advanced textbooks in control and signal processing
ISBN
1852330724 (alk. paper)
Bibliography, Etc. Note
Includes bibliographical references (p. [331]-342) and index.
Subject Added Entry-Topical Term
Computer algorithms. Genetic algorithms. Genetic algorithms. Computer algorithms.
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001 000045183297
005 20050802134101
008 050802s1999 enka b 001 0 eng
010 ▼a 98053817
015 ▼a GB99-16509
020 ▼a 1852330724 (alk. paper)
040 ▼a DLC ▼c DLC ▼d UKM ▼d OHX ▼d 211009
042 ▼a pcc
049 ▼a OCLC
050 0 0 ▼a QA76.9.A43 ▼b M36 1999
072 7 ▼a QA ▼2 lcco
082 0 0 ▼a 005.1 ▼2 21
090 ▼a 005.1 ▼b M266g
100 1 ▼a Man, K. F. ▼q (Kim F.) , ▼d 1951-.0 1.
245 1 0 ▼a Genetic algorithms: ▼b concepts and designs / ▼c K.F. Man, K.S. Tang, and S. Kwong.
260 ▼a London ; ▼a New York : ▼b Springer , ▼c c1999.
300 ▼a xii, 344 p. : ▼b ill. ; ▼c 24 cm. + ▼e 1 computer disk (3 1/2 in.).
440 0 ▼a Advanced textbooks in control and signal processing
504 ▼a Includes bibliographical references (p. [331]-342) and index.
650 0 ▼a Computer algorithms.
650 0 ▼a Genetic algorithms.
650 4 ▼a Genetic algorithms.
650 4 ▼a Computer algorithms.
700 1 ▼a Tang, K. S ▼d 1967-
700 1 ▼a Kwong, S ▼d 1959-
938 ▼a Otto Harrassowitz ▼b HARR ▼n har000663914 ▼c 82.00 DEM
945 ▼a KINS

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 005.1 M266g Accession No. 121108197 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

1. Introduction, Background and Biological Inspiration.- 1.1 Biological Background.- 1.1.1 Coding of DNA.- 1.1.2 Flow of Genetic Information.- 1.1.3 Recombination.- 1.1.4 Mutation.- 1.2 Conventional Genetic Algorithm.- 1.3 Theory and Hypothesis.- 1.3.1 Schema Theory.- 1.3.2 Building Block Hypothesis.- 1.4 A Simple Example.- 2. Modifications to Genetic Algorithms.- 2.1 Chromosome Representation.- 2.2 Objective and Fitness Functions.- 2.2.1 Linear Scaling.- 2.2.2 Sigma Truncation.- 2.2.3 Power Law Scaling.- 2.2.4 Ranking.- 2.3 Selection Methods.- 2.4 Genetic Operations.- 2.4.1 Crossover.- 2.4.2 Mutation.- 2.4.3 Operational Rates Settings.- 2.4.4 Reordering.- 2.5 Replacement Scheme.- 2.6 A Game of Genetic Creatures.- 2.7 Chromosome Representation.- 2.8 Fitness Function.- 2.9 Genetic Operation.- 2.9.1 Selection Window for Functions and Parameters.- 2.10 Demo and Run.- 3. Intrinsic Characteristics.- 3.1 Parallel Genetic Algorithm.- 3.1.1 Global GA.- 3.1.2 Migration GA.- 3.1.3 Diffusion GA.- 3.2 Multiple Objective.- 3.3 Robustness.- 3.4 Multimodal.- 3.5 Constraints.- 3.5.1 Searching Domain.- 3.5.2 Repair Mechanism.- 3.5.3 Penalty Scheme.- 3.5.4 Specialized Genetic Operations.- 4. Hierarchical Genetic Algorithm.- 4.1 Biological Inspiration.- 4.1.1 Regulatory Sequences and Structural Genes.- 4.1.2 Active and Inactive Genes.- 4.2 Hierarchical Chromosome Formulation.- 4.3 Genetic Operations.- 4.4 Multiple Objective Approach.- 4.4.1 Iterative Approach.- 4.4.2 Group Technique.- 4.4.3 Multiple-Objective Ranking.- 5. Genetic Algorithms in Filtering.- 5.1 Digital IIR Filter Design.- 5.1.1 Chromosome Coding.- 5.1.2 The Lowest Filter Order Criterion.- 5.2 Time Delay Estimation.- 5.2.1 Problem Formulation.- 5.2.2 Genetic Approach.- 5.2.3 Results.- 5.3 Active Noise Control.- 5.3.1 Problem Formulation.- 5.3.2 Simple Genetic Algorithm.- 5.3.3 Multiobjective Genetic Algorithm Approach.- 5.3.4 Parallel Genetic Algorithm Approach.- 5.3.5 Hardware GA Processor.- 6. Genetic Algorithms in H-infinity Control.- 6.1 A Mixed Optimization Design Approach.- 6.1.1 Hierarchical Genetic Algorithm.- 6.1.2 Application I: The Distillation Column Design.- 6.1.3 Application II: Benchmark Problem.- 6.1.4 Design Comments.- 7. Hierarchical Genetic Algorithms in Computational Intelligence.- 7.1 Neural Networks.- 7.1.1 Introduction of Neural Network.- 7.1.2 HGA Trained Neural Network (HGANN).- 7.1.3 Simulation Results.- 7.1.4 Application of HGANN on Classification.- 7.2 Fuzzy Logic.- 7.2.1 Basic Formulation of Fuzzy Logic Controller.- 7.2.2 Hierarchical Structure.- 7.2.3 Application I: Water Pump System.- 7.2.4 Application II: Solar Plant.- 8. Genetic Algorithms in Speech Recognition Systems.- 8.1 Background of Speech Recognition Systems.- 8.2 Block Diagram of a Speech Recognition System.- 8.3 Dynamic Time Warping.- 8.4 Genetic Time Warping Algorithm (GTW).- 8.4.1 Encoding mechanism.- 8.4.2 Fitness function.- 8.4.3 Selection.- 8.4.4 Crossover.- 8.4.5 Mutation.- 8.4.6 Genetic Time Warping with Relaxed Slope Weighting Function (GTW-RSW).- 8.4.7 Hybrid Genetic Algorithm.- 8.4.8 Performance Evaluation.- 8.5 Hidden Markov Model using Genetic Algorithms.- 8.5.1 Hidden Markov Model.- 8.5.2 Training Discrete HMMs using Genetic Algorithms.- 8.5.3 Genetic Algorithm for Continuous HMM Training.- 8.6 A Multiprocessor System for Parallel Genetic Algorithms.- 8.6.1 Implementation.- 8.7 Global GA for Parallel GA-DTW and PGA-HMM.- 8.7.1 Experimental Results of Nonlinear Time-Normalization by the Parallel GA-DTW.- 8.8 Summary.- 9. Genetic Algorithms in Production Planning and Scheduling Problems.- 9.1 Background of Manufacturing Systems.- 9.2 ETPSP Scheme.- 9.2.1 ETPSP Model.- 9.2.2 Bottleneck Analysis.- 9.2.3 Selection of Key-Processes.- 9.3 Chromosome Configuration.- 9.3.1 Operational Parameters for GA Cycles.- 9.4 GA Application for ETPSP.- 9.4.1 Case 1: Two-product ETPSP.- 9.4.2 Case 2: Multi-product ETPSP.- 9.4.3 Case 3: MOGA Approach.- 9.5 Concluding Remarks.- 10. Genetic Algori


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