We propose in this paper a novel approach to image fusion in which the fusion rule is guided by optimizing an image clarity

function. A Genetic Algorithm is used to stochastically select, relative to the clarity function, the optimum block from among the

source images. A novel nested Genetic Algorithm with gifted individuals found through bombardment of genes by the mutation

operator is designed and implemented. Convergence of the algorithm is analytically and empirically examined and statistically

compared (MANOVA) with the canonical GA using 3 test functions commonly used in the GA literature. The resulting GA is

invariant to parameters and population size, and a minimal size of 20 individuals is found to be sufficient in the tests. In the

fusion application, each individual in the population is a finite sequence of discrete values that represent input blocks.