DocumentCode :
775138
Title :
An introduction to genetic algorithms for electromagnetics
Author :
Haupt, Randy L.
Author_Institution :
Dept. of Electr. Eng., US Air Force Acad., Colorado Springs, CO, USA
Volume :
37
Issue :
2
fYear :
1995
fDate :
4/1/1995 12:00:00 AM
Firstpage :
7
Lastpage :
15
Abstract :
This article is a tutorial on using genetic algorithms to optimize antenna and scattering patterns. Genetic algorithms are “global” numerical-optimization methods, patterned after the natural processes of genetic recombination and evolution. The algorithms encode each parameter into binary sequences, called a gene, and a set of genes is a chromosome. These chromosomes undergo natural selection, mating, and mutation, to arrive at the final optimal solution. After providing a detailed explanation of how a genetic algorithm works, and a listing of a MATLAB code, the article presents three examples. These examples demonstrate how to optimize antenna patterns and backscattering radar-cross-section patterns. Finally, additional details about algorithm design are given
Keywords :
antenna radiation patterns; backscatter; binary sequences; complete computer programs; electrical engineering; electrical engineering computing; electromagnetic wave scattering; genetic algorithms; radar cross-sections; MATLAB code; algorithm design; antenna patterns optimisation; backscattering radar-cross-section patterns; binary sequences; chromosome; electromagnetics; evolution; gene; genetic algorithms; genetic recombination; mating; mutation; natural processes; natural selection; numerical-optimization methods; optimal solution; scattering patterns optimisation; tutorial; Algorithm design and analysis; Animals; Antenna radiation patterns; Biological cells; Electromagnetic forces; Electromagnetic scattering; Genetic algorithms; Radar scattering; Simulated annealing; Solid modeling;
fLanguage :
English
Journal_Title :
Antennas and Propagation Magazine, IEEE
Publisher :
ieee
ISSN :
1045-9243
Type :
jour
DOI :
10.1109/74.382334
Filename :
382334
Link To Document :
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