Genetic Algorithms for Control and Signal Processing (Advances in Industrial Control) - Hardcover

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.

This volume provides coverage of the field of genetic algorithms and their applications in solving engineering problems in control and signal processing. The basic genetic operations such as crossover, mutation and reinsertion are discussed, and the characteristics of genetic algorithms, their advantages and constraints, are also described. These processes are illustrated by real-world applications, including a report on the use of genetic algoritms for active noise control. The book closes with a description of a proposed hierarchical genetic algorithm designed to address the problems in determining system topology. The use of this formulation in digital filter design is discussed, and the idea is then extended to address neural network optimization and the construction of a reduced fuzzy membership set and rules for control and signal processing.