# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 3 Optimization
### The Use of GA  
The application of modern electromagnetic theory to radiation and scattering problems often requires the use of optimization. Among typical problems that require optimization are reflector design [156], object resonance extraction [157], and the development of an anti-reflective coating for a low radar cross section (RCS) [158]. Other tasks, such as the formation of the antenna array radiation pattern [159], which were solved without the use of optimization, are often easier to solve with optimization. This applies, in particular, when dealing with implementation limitations imposed by production considerations or environmental factors, differentiated regions, and/or discontinuous regions. In such situations, approximations or models of true electromagnetic phenomena are often used to save computational resources. These characteristics severely test the capabilities of many traditional optimization methods and often require their hybridization. For example, the use of GAs has become very common because of their special characteristics that make them an ideal tool that combines well with existing EM analysis methods and usually gives satisfying results. GAs are well suited for tasks where one needs to find an optimal solution among a large number of admissible ones. Consequently, in addition to the discreteness of the problem due to the large search space, GAs show good results when applied to this type of optimization problem. GAs are significantly more efficient and provide much faster convergence than random walk algorithms. In addition, they are easy to program and easy to implement. In contrast to gradient search, GA optimizers can easily handle discontinuous and undifferentiated functions. The use of evolutionary optimization strategies for EM design has also become widespread. Thus, in [160], a new method for optimizing the design of electromagnetic devices that uses GAs as a search method was presented. The method is used to optimize the pole shape of an electric motor. The electromagnetic analysis of the implemented devices is performed using FEM. The paper [161] presents an example of GA used for electromagnetic optimization. This work describes the development of lightweight broadband microwave absorbers, the design of antenna arrays with shaped-beam patterns, and the extraction of resonant modes of radar targets from backscatter response data. In addition, the paper [162] describes the basics of GAs, presents their history regarding their EM applications, describes the application of modern genetic operators to the field of electromagnetism, and presents the design results for various applications. The paper [163] presents a new optimization technique that combines classical and statistical methods in an innovative and efficient way. In particular, an evolutionary GA that uses a local minimization scheme based on the conjugate direction method was developed. The proposed optimizer can be applied to the design of planar microwave circuits and printed antenna arrays. The paper [164] presents the results of experiments to determine the optimal population size and mutation rate for a simple genetic algorithm. It was shown that the use of a small population size and relatively high mutation frequency is much preferable to the large population size and low mutation frequency used in most articles. The proliferation of wireless communication networks has created a natural interest in network optimization. In particular, the emphasis on low cost in many modern wireless networks often imposes severe limitations on the acceptable level of complexity of the transceiver processing circuitry. The available transmitter power in modern networks is also often high. Therefore, solving the design problem usually requires significant optimization in conjunction with network modeling. Using GAs, the authors in [165] managed to maximize the nodal signal-to-noise ratio (SNR) while minimizing transmitter power levels.