A hybrid algorithm using genetic algorithm and gradient-based algorithm for iterative microwave inverse scattering

We present a hybrid of genetic and gradient based algorithms, which efficiently finds the global optimum without getting stuck at local minima, for iterative microwave inverse scattering (IMIS). It utilizes the global convergence of genetic algorithms (GA) and the fine local tunability of gradient based algorithms through alternation so as to find the optimum or near optimum in reasonable computation time. We also propose a new crossover or 2N parent parameter wise crossover (N is the number of parameters) so that GA gives a faster convergence. Experimental results show that the hybrid algorithm can provide better performance efficiently