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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 algorithmsApplication of genetic algorithms to the design of broadband, wire, and integrated antennasGenetic algorithm— driven design of dielectric gratings and frequency-selective surfacesSynthesis of magnetostatic devices using genetic algorithmsApplication of genetic algorithms to multiobjective electromagnetic backscattering optimizationA comprehensive list of the up-to-date references applicable to electromagnetic design problemsSupplementedwith 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.

Genetic Algorithms in Electromagnetics

Human-based genetic algorithm - In evolutionary computation, a human-based genetic algorithm (HBGA) is a genetic algorithm that allows humans to contribute their innovative solutions to the evolutionary process. For this purpose HBGA uses human-based innovation interfaces for initialization, mutation, and crossover operators.

Interactive genetic algorithm - Interactive genetic algorithm (IGA) is defined as a genetic algorithm that uses human evaluation. These algorithms belong to a more general category of Interactive evolutionary computation.

Genetic algorithm - A genetic algorithm (GA) is a search technique used in computer science to find approximate solutions to optimization and search problems. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, natural selection, and recombination (or crossover).

Crossover (genetic algorithm) - In genetic algorithms, crossover is a genetic operator used to vary the programming of a chromosome or chromosomes from one generation to the next. It is an analogy to reproduction and biological crossover, upon which genetic algorithms are based.

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