1 Introduction
The spectrum of antenna designs for applications in communication, radar, and remote sensing systems is vast, and there is an increasing need for high-performance, customized antennas. Current methods of designing and optimizing antennas by hand are time and labor intensive, limit complexity, increase the time and cost expended, and require that antenna engineers have significant knowledge of the universe of antenna designs. Designers are aided by local optimization techniques, however an initial guess that is close to the final design is required. Evolutionary algorithms such as the genetic algorithm (GA) [8], [7], do not require an initial guess and the amount of design parameters that an engineer must provide can be very minimal. Evolutionary algorithms show promise because they have proven ability to search large, unknown design spaces.