Evolutionary-based Optimization Techniques for Inverse Scattering: A Review

The use of stochastic global optimizers has had a non‐negligible impact on several areas of research and industry and they have been effectively applied to several problems in engineering and sciences [1]. Thanks to the availability and growing of computational resources with the large diffusion of modern computers, optimization techniques based on Evolutionary Algorithms (EAs) have received a wide attention because of their attractive features. As a matter of fact, EAs are hill‐climbing algorithms and do not require the differentiation of the cost function, which is a must" for gradientbased methods. They are based on stochastic iterative procedures where a pool of trial solutions is used to sample the solution space at each iteration thus improving the search capability as compared to single‐agent techniques (e.g.