Genetic algorithms have become very important in many fields. Genetic Algorithms in Electromagnetics provides a tutorial of the application of genetic algorithms to problems in electromagnetics. It will provide up-to-date developments in genetic algorithms and a review of the many applications to electromagnetics problems.

Preface. Acknowledgments. 1. Introduction to Optimization in Electromagnetics. 1.1 Optimizing a Function of One Variable. 1.1.1 Exhaustive Search. 1.1.2 Random Search. 1.1.3 Golden Search. 1.1.4 Newton’s Method. 1.1.5 Quadratic Interpolation. 1.2 Optimizing a Function of Multiple Variables. 1.2.1 Random Search. 1.2.2 Line Search. 1.2.3 Nelder–Mead Downhill Simplex Algorithm. 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. 2.4.1 Roulette Wheel Selection. 2.4.2 Tournament 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. 4.2.1 Optimum Quantized Low-Sidelobe Phase Tapers. 4.2.2 Phase-Only Array Synthesis Using Adaptive GAs. 4.3 Optimizing Arrays with Complex Weighting. 4.3.1 Shaped-Beam Synthesis. 4.3.2 Creating a Plane Wave in the Near Field. 4.4 Optimizing Array Element Spacing. 4.4.1 Thinned Arrays. 4.4.2 Interleaved Thinned Linear Arrays. 4.4.3 Array Element Perturbation. 4.4.4 Aperiodic Fractile Arrays. 4.4.5 Fractal–Random and Polyfractal Arrays. 4.4.6 Aperiodic Refl ectarrays. 4.5 Optimizing Conformal Arrays. 4.6 Optimizing Reconfi gurable Apertures. 4.6.1 Planar Reconfi gurable Cylindrical Wire Antenna Design. 4.6.2 Planar Reconfi gurable Ribbon Antenna Design. 4.6.3 Design of Volumetric Reconfi gurable Antennas. 4.6.4 Simulation Results—Planar Reconfi gurable Cylindrical Wire Antenna. 4.6.5 Simulation Results—Volumetric Reconfi gurable Cylindrical Wire Antenna. 4.6.6 Simulation Results—Planar Reconfi gurable Ribbon Antenna. 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. 8.2.1 Optimization of FSS Filters. 8.2.2 Optimization of Reconfi gurable FSSs. 8.2.3 Optimization of EBGs. 8.3 Scattering from Absorbers. 8.3.1 Conical or Wedge Absorber Optimization. 8.3.2 Multilayer Dielectric Broadband Absorber Optimization. 8.3.3 Ultrathin Narrowband Absorber Optimization. 9. GA Extensions. 9.1 Selecting Population Size and Mutation Rate. 9.2 Particle Swarm Optimization (PSO). 9.3 Multiple-Objective Optimization. 9.3.1 Introduction. 9.3.2 Strength Pareto Evolutionary Algorithm—Strength Value Calculation. 9.3.3 Strength Pareto Evolutionary Algorithm—Pareto Set Clustering. 9.3.4 Strength Pareto Evolutionary Algorithm—Implementation. 9.3.5 SPEA-Optimized Planar Arrays. 9.3.6 SPEA-Optimized Planar Polyfractal Arrays. Appendix: MATLAB® Code. Bibliography. Index.

A thorough and insightful introduction to using genetic algorithms to optimize electromagnetic systems 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® commandsTwo step-by-step examples of optimizing antenna arrays as well as a comprehensive overview of applications of GA to antenna array design problemsCoverage of GA as an adaptive algorithm, including adaptive and smart arrays as well as adaptive reflectors and crossed dipolesExplanations 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 antennasCoverage of GA optimization of scattering, including scattering from frequency selective surfaces and electromagnetic band gap materialsIdeas on operator and parameter selection for a GADetailed explanations of particle swarm optimization and multiple objective optimizationAn appendix of MATLAB code for experimentation