Electromagnetic Optimization by Genetic Algorithms

Authoritative coverage of a revolutionary technique for overcoming problems in electromagnetic design Genetic algorithms are stochastic search procedures modeled on the Darwinian concepts of natural selection and evolution. The machinery of genetic algorithms utilizes an optimization methodology that allows a global search of the cost surface via statistical random processes dictated by the Darwinian evolutionary concept. These easily programmed and readily implemented procedures robustly locate extrema of highly multimodal functions and therefore are particularly well suited to finding solutions to a broad range of electromagnetic optimization problems. Electromagnetic Optimization by Genetic Algorithms is the first book devoted exclusively to the application of genetic algorithms to electromagnetic device design. Compiled by two highly competent and well–respected members of the electromagnetics community, this book describes numerous applications of genetic algorithms to the design and optimization of various low– and high–frequency electromagnetic components. Special features include:
∗ Introduction by David E. Goldberg, "A Meditation on the Application of Genetic Algorithms"
∗ Design of linear and planar arrays using genetic algorithms
∗ Application of genetic algorithms to the design of broadband, wire, and integrated antennas
∗ Genetic algorithm–driven design of dielectric gratings and frequency–selective surfaces
∗ Synthesis of magnetostatic devices using genetic algorithms
∗ Application of genetic algorithms to multiobjective electromagnetic backscattering optimization
∗ A comprehensive list of the up–to–date references applicable to electromagnetic design problems
Supplemented with more than 250 illustrations, Electromagnetic Optimization by Genetic Algorithms is a powerful resource for electrical engineers interested in modern electromagnetic desig ns and an indispensable reference for university researchers.

Spis treści:
An Introduction to Genetic Algorithms (J. Johnson & Y. Rahmat–Samii).
Genetic Algorithms: Theory and Advanced Techniques (D. Weile & E. Michielssen).
Rules of Thumb for GA Parameters, GA Monitoring, GA Parameter Optimization, and Enhancement of GA Efficiency (D. Linden).
Thinned Arrays Using Genetic Algorithms (R. Haupt).
Application of Genetic Algorithms and Simulated Annealing to Some Antenna Problems (F. Ares–Pena).
Synthesis of Linear and Planar Arrays Using Genetic Algorithms (D. Marcano & F. Durán).
Application of the Genetic Algorithm to Broadband Antenna Design (Z. Altman, et al.).
Design of Wire Antennas Using Genetic Algorithms (E. Altshuler & D. Linden).
Evolutionary Designs of Integrated Antennas Using Genetic Algorithms and Methods of Moments (GA/MoM) (J. Johnson & Y. Rahmat–Samii).
Optimization and Synthesis of Various Printed Antennas (M. Himdi & J.–P. Daniel).
The Application of Domain Decomposition Genetic Algorithms to Pseudostratified Electromagnetic Filtering Structures (D. Weile E. Michielssen).
Designing Dielectric Grating Filters with PGAPACK (C. Zuffada, et al.).
Genetic Algorithms for Electromagnetic Backscattering: Multiobjective Optimization (M.–O. Bristeau, et al.).
Optimal Design of Magnetostatic Devices: The Genetic Algorithm Approach and System Optimization Strategies (O. Mohammed).
Bibliography.
Index.

Nota biograficzna:
YAHYA RAHMAT–SAMII, PhD, is Professor of Electrical Engineering at the University of California, Los Angeles. ERIC MICHIELSSEN, PhD, is a professor in the Department of Electrical and Computer Engineering at the University of lllinois at Urbana–Champaign.

Okładka tylna:
Authoritative coverage of a revolutionary technique for overcoming problems in electromagnetic design Genetic algorithms are stochastic search procedures modeled on the Darwinian concepts of natural selection and evolution. The machinery of genetic algorithms utilizes an optimization methodology that allows a global search of the cost surface via statistical random processes dictated by the Darwinian evolutionary concept. These easily programmed and readily implemented procedures robustly locate extrema of highly multimodal functions and therefore are particularly well suited to finding solutions to a broad range of electromagnetic optimization problems. Electromagnetic Optimization by Genetic Algorithms is the first book devoted exclusively to the application of genetic algorithms to electromagnetic device design. Compiled by two highly competent and well–respected members of the electromagnetics community, this book describes numerous applications of genetic algorithms to the design and optimization of various low– and high–frequency electromagnetic components. Special features include:
∗ Introduction by David E. Goldberg, "A Meditation on the Application of Genetic Algorithms"
∗ Design of linear and planar arrays using genetic algorithms
∗ Application of genetic algorithms to the design of broadband, wire, and integrated antennas
∗ Genetic algorithm–driven design of dielectric gratings and frequency–selective surfaces
∗ Synthesis of magnetostatic devices using genetic algorithms
∗ Application of genetic algorithms to multiobjective electromagnetic backscattering optimization
∗ A comprehensive list of the up–to–date references applicable to electromagnetic design problems
Supplemented with more than 250 illustrations, Electromagnetic Optimization by Genetic Algorithms is a powerful resource for electrical engineers interested in modern electromagnetic designs and an indispensable reference for university researchers.