## Electromagnetic Optimization by Genetic AlgorithmsAuthoritative 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. |

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### Contents

An Introduction to Genetic Algorithms | 1 |

A Simple Genetic Algorithm | 8 |

GA Operators | 15 |

Copyright | |

39 other sections not shown

### Common terms and phrases

amplitude analysis antenna array antenna design Antennas Propag application approach binary bits calculated chromosomes coefficients computed configuration convergence crossover crossover operator described dielectric dipole electromagnetic elements encoding Equation evaluation excitation field FIGURE filter fitness function fitness value frequency GA/MoM gain gene genetic algorithm given Gray code ground plane hybrid IEEE Trans individual integral iterations length linear array loaded main beam matrix maximum method method of moments Michielssen microwave monopole monopole antenna mutation Nash equilibrium NEC2 nulls objective function objective function value obtained optimization problems optimum parameters parents Pareto patch antenna performance PGAPACK phase planar arrays population radiation pattern Rahmat-Samii random range relative sidelobe level resonance schemata scheme search space shown in Fig simulated annealing simulations slot solution specific string structure sum pattern synthesis techniques thinned array tion variables vector VSWR wire antennas Yagi Yagi antenna Z-matrix

### References to this book

Computer Engineering in Applied Electromagnetism Slawomir Wiak,A. Krawczyk,M. Trlep No preview available - 2005 |