Genetic Algorithms in Engineering Systems

by A.M.S. Zalzala, Peter Fleming

Overview

This book comprises ten invited expert contributions on the theory and applications of genetic algorithms in a variety of engineering systems. In addition to addressing the simple formulation of GAs, the chapters include original material on the design of evolutionary algorithms for particular engineering applications. Chosen for their experience in the field, the authors are drawn from both academia and industry worldwide, and provide extensive insight into their respective fields. The volume is suitable for researchers and postgraduates who need to be up-to-date with developments in this important subject, as well as practitioners in industry who are eager to find out how to solve their particular real-life problems.

Product Details

ISBN-13:	9780852969021
Publisher:	The Institution of Engineering and Technology
Publication date:	06/30/1997
Series:	Control, Robotics and Sensors
Pages:	280
Product dimensions:	6.14(w) x 9.21(h) x (d)

About the Author

Ali Zalzala is a lecturer in control engineering at the Universityof Sheffield with particular interest in industrial automation. Through the Robotics Research Group, he manages an active programme on the advanced real-time control of intelligent systems. In addition to the development of industry-supported systems, research is carried out on the applications of soft computing techniques and distributed processing in robotics, manufacturing processes and prosthetic devices. His work is supported by grants from EPSRC, ESPRIT as well as industry. Over the last few years, he has published over 70 papers in journals and international conference proceedings.

Peter Fleming is a professor of industrial systems and control at the Universityof Sheffield and head of the Department of Automatic Control and Systems Engineering. His control and systems engineering interests include control applications of genetic algorithms and optimisation, software for control system design and implementation, and distributed and parallel processing for real-time control and instrumentation. These interests have led to the development of close links with a variety of industries in sectors such as aerospace, power generation, food processing and manufacturing. He is director of the Rolls-Royce UniversityTechnology Centre for Control and Systems Engineeringand has authored over 150 publications, including four books.

Table of Contents

• Chapter 1: Introduction to genetic algorithms
• Chapter 2: Levels of evolution for control systems
• Chapter 3: Multiobjective genetic algorithms
• Chapter 4: Constraint resolutions in genetic algorithms
• Chapter 5: Towards the evolution of scaleable neural architectures
• Chapter 6: Chaotic systems identifcation
• Chapter 7: Job shop scheduling
• Chapter 8: Evolutionary algorithms for robotic systems: principles and implemenations
• Chapter 9: Aerodynamic inverse optimisation problems
• Chapter 10: Genetic design of VLSI layouts

Editorial Reviews

Nineteen contributors from academia and industry overview the fundamentals of genetic algorithms, a stochastic global search model of biological evolution; contrast GAs with more traditional computer search and optimization methods, and with multiobjective genetic algorithms (MOGAs); discuss new theories regarding constraint resolutions, neural networks, chaotic systems identification, and levels of evolution for control systems; and present engineering/manufacturing applications (e.g. aerodynamic wing design, gas turbine engine control, genetic design of VLSI layouts, job shop scheduling, robotics, and signal processing). SAMUEL, a machine learning program, embodies some of the parameters considered. Annotation c. by Book News, Inc., Portland, Or.

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