Genetic design of linear antenna arrays

Discusses a way to optimize both the topology and the numerical parameters of an antenna design. The approach relies on an “antenna language” to define how antennas are constructed, and a genetic algorithm to create new designs using this language. The grammatical rules of a language can be very vague or very specific, depending on the purpose of the designer. With a vague grammar, genetic algorithms search a very large design space, and can occasionally find unexpected solutions to a design problem. Other times, they completely fail to find a reasonable solution because of the vastness of the search space. In this case, including knowledge about the problem into the grammar narrows the search to a region expected to yield good results. This yields more conventional design solutions that usually perform reasonably well. In an example, two languages were used to design a linear antenna array. The general language allowed a wide variety of designs, while the Yagi-log language confined the search to topologies known to perform well. The performance of the antennas produced by both languages was superior to that of a conventional log-periodic design. Further, the Yagi-log design was more fit than the unconventional design from the general language, illustrating the benefits of including knowledge in the grammar