%D 2011
%X 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., Simulated Annealing). A?priori information can also be easily introduced in terms of additional constraints on the actual solution or the boundaries of the solution space. Moreover, they can directly deal with real values as well as with coded representations of the unknowns (e.g., binary coding). Their main drawback (i.e., the convergence rate) has been also further contrasted by exploiting their implicit and explicit parallelism thanks to modern computer clusters [2].
%A Paolo Rocca
%A Andrea Massa
%I University of Trento
%T Evolutionary-based Optimization Techniques for Inverse Scattering: A Review
%L elediasc12513