News Events Links Contact us

Antennas Community

ACE Network


ACE Network ACE Results ARTIC Project Books Conferences Journals Open Positions Other Events Publications

Title Authors Publisher Year
Genetic Algorithms in Electromagnetics Randy L. Haupt, Douglas H. Werner Wiley-IEEE Press 2007


Genetic Algorithms in Electromagnetics

Randy L. Haupt, Douglas H. Werner

Wiley-IEEE Press


301 pages





Genetic Algorithms in Electromagnetics focuses on optimizing the objective function when a computer algorithm, analytical model, or experimental result describes the performance of an electromagnetic system. It offers expert guidance to optimizing electromagnetic systems using genetic algorithms (GA), which have proven to be tenacious in finding optimal results where traditional techniques fail.


Genetic Algorithms in Electromagnetics begins with an introduction to optimization and several commonly used numerical optimization routines, and goes on to feature:

  • Introductions to GA in both binary and continuous variable forms, complete with examples of MATLAB(r) commands
  • Two step-by-step examples of optimizing antenna arrays as well as a comprehensive overview of applications of GA to antenna array design problems
  • Coverage of GA as an adaptive algorithm, including adaptive and smart arrays as well as adaptive reflectors and crossed dipoles
  • Explanations of the optimization of several different wire antennas, starting with the famous "crooked monopole"
  • How to optimize horn, reflector, and microstrip patch antennas, which require significantly more computing power than wire antennas
  • Coverage of GA optimization of scattering, including scattering from frequency selective surfaces and electromagnetic band gap materials
  • Ideas on operator and parameter selection for a GA
  • Detailed explanations of particle swarm optimization and multiple objective optimization

·          An appendix of MATLAB code for experimentation



Table of Contents




1. Introduction to Optimization in Electromagnetics.

1.1 Optimizing a Function of One Variable.

1.2 Optimizing a Function of Multiple Variables.

1.3 Comparing Local Numerical Optimization Algorithms.

1.4 Simulated Annealing.

1.5 Genetic Algorithm.

2. Anatomy of a Genetic Algorithm.

2.1 Creating an Initial Population.

2.2 Evaluating Fitness.

2.3 Natural Selection.

2.4 Mate Selection.

2.5 Generating Offspring.

2.6 Mutation.

2.7 Terminating the Run.

3. Step-by-Step Examples.

3.1 Placing Nulls.

3.2 Thinned Arrays.

4. Optimizing Antenna Arrays.

4.1 Optimizing Array Amplitude Tapers.

4.2 Optimizing Array Phase Tapers.

4.4 Optimizing Array Element Spacing.

4.5 Optimizing Conformal Arrays.

4.6 Optimizing Reconfi gurable Apertures.

5. Smart Antennas Using a GA.

5.1 Amplitude and Phase Adaptive Nulling.

5.2 Phase-Only Adaptive Nulling.

5.3 Adaptive Reflector.

5.4 Adaptive Crossed Dipoles.

6. Genetic Algorithm Optimization of Wire Antennas.

6.1 Introduction.

6.2 GA Design of Electrically Loaded Wire Antennas.

6.3 GA Design of Three-Dimensional Crooked-Wire Antennas.

6.4 GA Design of Planar Crooked-Wire and Meander-Line Antennas.

6.5 GA Design of Yagi–Uda Antennas.

7. Optimization of Aperture Antennas.

7.1 Refl ector Antennas.

7.2 Horn Antennas.

7.3 Microstrip Antennas.

8. Optimization of Scattering.

8.1 Scattering from an Array of Strips.

8.2 Scattering from Frequency-Selective Surfaces.

8.3 Scattering from Absorbers.

9. GA Extensions.

9.1 Selecting Population Size and Mutation Rate.

9.2 Particle Swarm Optimization (PSO).

9.3 Multiple-Objective Optimization.

Appendix: MATLAB® Code.




File Size Date
No attachments
« Back to area
Focus On

Antenna Research and Technology for the Intelligent Car

Jobs and Open Positions
VCE Download Area Summary, Leaflet, Poster

Antennas VCE for ACE-1 Project (2004-2005)
FP6: Sixth Framework Programme © 2006 Antennas VCE. All rights reserved. Information Society Technologies