The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology impacts all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies, . . . , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. The emerging technologies in control include fuzzy logic, intelligent control, neural networks and hardware developments like micro-electro-mechanical systems and autonomous vehicles. This volume describes the biological background, basic construction and application of the emerging technology of Genetic Algorithms. Dr Kim Man and his colleagues have written a book which is both a primer introducing the basic concepts and a research text which describes some of the more advanced applications of the genetic algorithmic method. The applications described are especially useful since they indicate the power of the GA method in solving a wide range of problems. These sections are also instructive in showing how the mechanics of the GA solutions are obtained thereby acting as a template for similar types of problems. The volume is a very welcome contribution to the Advances in Industrial Control Series. M. J. Grimble and M. A.
Reihe
Auflage
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Illustrationen
Maße
Höhe: 23.5 cm
Breite: 15.5 cm
Gewicht
ISBN-13
978-3-540-76101-3 (9783540761013)
DOI
10.1007/978-1-4471-0955-6
Schweitzer Klassifikation
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. Modification in Genetic Algorithm.- 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.- 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. Advanced GA Applications.- 4.1 Case Study 1: GA in Time Delay Estimation.- 4.1.1 Problem Formulation.- 4.1.2 Genetic Approach.- 4.1.3 Results.- 4.2 Case Study 2: GA in Active Noise Control.- 4.2.1 Problem Formulation.- 4.2.2 Simple Genetic Algorithm.- 4.2.3 Multiobjective Genetic Algorithm Approach.- 4.2.4 Parallel Genetic Algorithm Approach.- 4.2.5 Hardware GA Processor.- 4.3 Case Study 3: GA in Automatic Speech Recognition.- 4.3.1 Warping Path.- 4.3.2 Implementation of Genetic Time Warping.- 4.3.3 Performance Evaluation.- 5. Hierarchical Genetic Algorithm.- 5.1 Biological Inspiration.- 5.1.1 Regulatory Sequences and Structural Genes.- 5.1.2 Active and Inactive Genes.- 5.2 Hierarchical Chromosome Formulation.- 5.3 Genetic Operations.- 5.4 Multiple Objective Approach.- 5.4.1 Iterative Approach.- 5.4.2 Group Technique.- 5.4.3 Multiple-Objective Ranking.- 6. Filtering Optimization.- 6.1 Digital IIR Filter Design.- 6.1.1 Chromosome Coding.- 6.1.2 The Lowest Filter Order Criterion.- 6.2 H-infinity Controller Design.- 6.2.1 A Mixed Optimization Design Approach.- 6.2.2 Hierarchical Genetic Algorithm.- 6.2.3 The Distillation Column Design.- 6.2.4 Design Comments.- 7. Emerging Technology.- 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 Experimental Results.- Appendix A.- Appendix B.- Appendix C.- Appendix D.- Appendix E.- Appendix F.- References.
