Genetic Algorithms

Concepts and Designs

AvKim-Fung Man,Kit-Sang Tang

Häftad, Engelska, 1999

Del i serien Advanced Textbooks in Control and Signal Processing

536 kr

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Beskrivning

The practical application of Genetic Algorithms to the solution of engineering problems, is rapidly becoming an established approach in the fields of control and signal processing. This book 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. These features are fully illustrated by real-world applications. Also described is a newly proposed and unique hierarchical genetic algorithm designed to address the problems in determining system topology. For added value, a 3.5" disk accompanies the book, that provides the reader with an interactive Genetic Algorithms demonstration programme.

Produktinformation

Utgivningsdatum:1999-02-25
Mått:155 x 235 x undefined mm
Format:Häftad
Språk:Engelska
Serie:Advanced Textbooks in Control and Signal Processing
Antal sidor:344
Upplaga:2
Förlag:Springer London Ltd
ISBN:9781852330729

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Recensioner i media

From the reviews: This superb book is suitable for readers from a wide range of disciplines. Assembly Automation 20 (2000) 86 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 March (2002) 366 (Reviewer: William F. Fulkerson) 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 19 (2005) 59 - 62 (Reviewer: Doris Saez)

Innehållsförteckning

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.- 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.- 10.3 Wireles Local Area Network Design.- Appendix A.- Appendix B.- Appendix C.- Appendix D.- Appendix E.- Appendix F.- References.