I. Introduction
With the increasing popularity of evolutionary optimization methods and the easy availability of unprecedented computational resources, solving complex non-linear Electromagnetic (EM) problems has become more accessible. Evolutionary Algorithms (EAs) can achieve an optimal solution with a high probability for complex high-dimensional EM problems, unlike the manual design approach, which is time-consuming and often produces sub-optimal solutions [1]. Due to strong mutual coupling and other propagation effects, optimizing antenna and EM components involves highly non-linear objective functions that exhibit an epistatic behavior [2]. Such complex antenna and EM component design require a full-wave simulation-based derivative-free optimization approach. Evolutionary Algorithms are a class of derivative-free optimization with superior exploration skills, and they can handle multi-objective, multi-dimensional, and multi-extremal optimization problems. EAs are conducive to parallel computing, which significantly reduces the optimization run time by distributing the task among multiple computers. In conjunction with full-wave EM solvers, EAs automate the antenna design process, which brings a sharp reduction in the cost of design and production [1], [3].