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Speeding convergence of genetic algorithms for inverse scattering problems

As a global optimization method, genetic algorithm (GA) is very convenient for continuous and discrete optimizations, especially for inverse scattering problems, and many remarkable results have been obtained. One of the problems of GA is how to achieve the evolution toward the global minimum efficiently. This paper introduces the techniques of mutating genes and seeding population to speed GA convergence.