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Two modifications of G enetic algorithm (GA) are proposed that employ gradient analysis of the fitness function and are integrated with the main genetic procedure. Combination of the relative weighted error factor and adaptive size of the mutation pool accelerates convergence of the iterative process and indicates when the global optimum solution is found. Local gradient correction of the initial pool during iterations refines the search procedure. Computational experiments show that both modifications can increase efficiency of GA when they are applied to an image registration problem.

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