  To facilitate the antenna design with the aid of computer, one of the
practices in consumer electronic industry is to model and optimize antenna
performances with a simplified antenna geometric scheme. Traditional antenna
modeling requires profound prior knowledge of electromagnetics in order to
achieve a good design which satisfies the performance specifications from both
antenna and product designs. The ease of handling multidimensional optimization
problems and the less dependence on domain knowledge and experience are the key
to achieve the popularity of simulation driven antenna design and optimization
for the industry. In this paper, we introduce a flexible geometric scheme with
the concept of mesh network that can form any arbitrary shape by connecting
different nodes. For such problems with high dimensional parameters, we propose
a machine learning based generative method to assist the searching of optimal
solutions. It consists of discriminators and generators. The discriminators are
used to predict the performance of geometric models, and the generators to
create new candidates that will pass the discriminators. Moreover, an
evolutionary criterion approach is proposed for further improving the
efficiency of our method. Finally, not only optimal solutions can be found, but
also the well trained generators can be used to automate future antenna design
and optimization. For a dual resonance antenna design with wide bandwidth, our
proposed method is in par with Trust Region Framework and much better than the
other mature machine learning algorithms including the widely used Genetic
Algorithm and Particle Swarm Optimization. When there is no wide bandwidth
requirement, it is better than Trust Region Framework.
