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Most range image registration techniques are based on variants of the ICP (iterative closest point) algorithm. The ICP algorithm has two main drawbacks, the possibility of convergence to a local minimum and the need to prealign the images. Genetic algorithms (GAs) are known to be robust in relation to search and optimization problems and were recently applied to range image registration, providing good convergence results without the constraints observed in the ICP approaches. To improve range image registration by GAs, we explored 3 novel approaches: a hybrid algorithm that combines a GA with hillclimbing heuristics (GH), a parallel migration GA (MGA), and a MGA using hillclimbing (MGH). We also define a new robust evaluation measure, called the surface interpenetration, to compare the obtained registration results. Up to now, interpenetration has been evaluated only qualitatively; we define the first quantitative measure for it. The experimental results show that our methods yield more accurate registration results than either ICP or standard GA approaches.