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140122 ||| eng |
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|a 9781447105770
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| 100 |
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|a Man, Kim-Fung
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| 245 |
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|a Genetic Algorithms
|h Elektronische Ressource
|b Concepts and Designs
|c by Kim-Fung Man, Kit-Sang Tang, Sam Kwong
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| 250 |
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|a 1st ed. 1999
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| 260 |
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|a London
|b Springer London
|c 1999, 1999
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| 300 |
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|a XII, 344 p. 94 illus
|b online resource
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|a 1. Introduction, Background and Biological Inspiration -- 1.1 Biological Background -- 1.2 Conventional Genetic Algorithm -- 1.3 Theory and Hypothesis -- 1.4 A Simple Example -- 2. Modifications to Genetic Algorithms -- 2.1 Chromosome Representation -- 2.2 Objective and Fitness Functions -- 2.3 Selection Methods -- 2.4 Genetic Operations -- 2.5 Replacement Scheme -- 2.6 A Game of Genetic Creatures -- 2.7 Chromosome Representation -- 2.8 Fitness Function -- 2.9 Genetic Operation -- 2.10 Demo and Run -- 3. Intrinsic Characteristics -- 3.1 Parallel Genetic Algorithm -- 3.2 Multiple Objective -- 3.3 Robustness -- 3.4 Multimodal -- 3.5 Constraints -- 4. Hierarchical Genetic Algorithm -- 4.1 Biological Inspiration -- 4.2 Hierarchical Chromosome Formulation -- 4.3 Genetic Operations -- 4.4 Multiple Objective Approach -- 5. Genetic Algorithms in Filtering -- 5.1 Digital IIR Filter Design -- 5.2 Time Delay Estimation -- 5.3 Active Noise Control --
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|a 6. Genetic Algorithms in H-infinity Control -- 6.1 A Mixed Optimization Design Approach -- 7. Hierarchical Genetic Algorithms in Computational Intelligence -- 7.1 Neural Networks -- 7.2 Fuzzy Logic -- 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) -- 8.5 Hidden Markov Model using Genetic Algorithms -- 8.6 A Multiprocessor System for Parallel Genetic Algorithms -- 8.7 Global GA for Parallel GA-DTW and PGA-HMM -- 8.8 Summary -- 9. Genetic Algorithms in Production Planning and Scheduling Problems -- 9.1 Background of Manufacturing Systems -- 9.2 ETPSP Scheme -- 9.3 Chromosome Configuration -- 9.4 GA Application for ETPSP -- 9.5 Concluding Remarks -- 10. Genetic Algorithms in Communication Systems -- 10.1 Virtual Path Design in ATM -- 10.2 Mesh Communication Network Design --
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|a 10.3 Wireles Local Area Network Design -- Appendix A -- Appendix B -- Appendix C -- Appendix D -- Appendix E -- Appendix F -- References
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| 653 |
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|a Industrial engineering
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| 653 |
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|a Electrical and Electronic Engineering
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Control and Systems Theory
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| 653 |
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|a Electrical engineering
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| 653 |
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|a Industrial and Production Engineering
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| 653 |
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|a Signal, Speech and Image Processing
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| 653 |
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|a Control engineering
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Applications of Mathematics
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| 653 |
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|a Signal processing
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| 653 |
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|a Mathematics
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| 653 |
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|a Production engineering
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| 700 |
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|a Tang, Kit-Sang
|e [author]
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| 700 |
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|a Kwong, Sam
|e [author]
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
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|a Advanced Textbooks in Control and Signal Processing
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| 028 |
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|a 10.1007/978-1-4471-0577-0
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| 856 |
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|u https://doi.org/10.1007/978-1-4471-0577-0?nosfx=y
|x Verlag
|3 Volltext
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|a 519
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| 520 |
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|a The practical application of genetic algorithms to the solution of engineering problems, has rapidly become an established approach in the fields of control and signal processing. Genetic Algorithms provides comprehensive coverage of the techniques involved, describing the intrinsic characteristics, advantages and constraints of genetic algorithms, as well as discussing genetic operations such as crossover, mutation and reinsertion. In addition, the principle of multiobjective optimization and computing parallelism are discussed. The use of genetic algorithms in many areas of interest in control and signal processing is detailed; among the areas of application are: • filtering; • H-infinity control; • speech recognition; • production planning and scheduling; • computational intelligence; and • communication systems. Also described is an original hierarchical genetic algorithm designed to address the problems in determining system topology. The authors provide "A Game of Genetic Creatures", a fundamental study for GA based on computer-generated insects to demonstrate some of the ideas developed in the text as a download available from www.springer.com/1-85233-072-4. This superb book is suitable for readers from a wide range of disciplines. Assembly Automation This is a well-written engineering textbook. Genetic algorithms are properly explained and well motivated. The engineering examples illustrate the power of application of genetic algorithms. Journal of the American Statistical Association The book is a good contribution to the genetic algorithm area from an applied point of view. It should be read by engineers, undergraduate or postgraduate students and researchers. International Journal of Adaptive Control and Signal Processing
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