Genetic Algorithms

Concepts and Designs
1. Auflage 1999. Corr. 2. printing 2001
ISBN: 978-1-85233-072-9
Verlag: Springer

Buch, Englisch, 344 Seiten, Book w. online files / update, Format (B × H): 155 mm x 235 mm, Gewicht: 546 g

Reihe: Advanced Textbooks in Control and Signal Processing

This comprehensive book gives a overview of the latest discussions in the application of genetic algorithms to solve engineering problems. Featuring real-world applications and an accompanying disk, giving the reader the opportunity to use an interactive genetic algorithms demonstration program.

Zielgruppe

Graduate

Autoren/Hrsg.

Man, Kim-Fung

Kwong, Sam

Tang, Kit-Sang

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Technische Wissenschaften Technik Allgemein Technik: Allgemeines
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein

Inhaltsverzeichnis

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.