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.

Inhalt

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. Modification in Genetic Algorithm.- 2.1 Chromosome Representation.- 2.2 Objective and Fitness Functions.- 2.3 Selection Methods.- 2.4 Genetic Operations.- 2.5 Replacement Scheme.- 3. Intrinsic Characteristics.- 3.1 Parallel Genetic Algorithm.- 3.2 Multiple Objective.- 3.3 Robustness.- 3.4 Multimodal.- 3.5 Constraints.- 4. Advanced GA Applications.- 4.1 Case Study 1: GA in Time Delay Estimation.- 4.2 Case Study 2: GA in Active Noise Control.- 4.3 Case Study 3: GA in Automatic Speech Recognition.- 5. Hierarchical Genetic Algorithm.- 5.1 Biological Inspiration.- 5.2 Hierarchical Chromosome Formulation.- 5.3 Genetic Operations.- 5.4 Multiple Objective Approach.- 6. Filtering Optimization.- 6.1 Digital IIR Filter Design.- 6.2 H-infinity Controller Design.- 7. Emerging Technology.- 7.1 Neural Networks.- 7.2 Fuzzy Logic.- Appendix A.- Appendix B.- Appendix C.- Appendix D.- Appendix E.- Appendix F.- References.