A novel genetic operator called cloning is introduced and tested in different applications of genetic algorithms. Essentially, the cloning monotonically increases the lengths of the chromosomes during the evolution. It is argued that, under these circumstances, the cloning operator can accommodate a multiresolution search strategy, where the search starts at coarser scales and is subsequently mapped to finer scales upon achieving some in-scale performance criteria. Although the practical implementation of cloning is application dependent, a few general requirements are stated. In the remainder of the paper, different implementations of the cloning operator are introduced and employed in distinct applications, namely, function optimization, object support reconstruction from the support of its autocorrelation and the shortest path problem in planar graphs. The first two cases present typical multiresolution approaches to search problems and their results show consistent improvements in convergence speed with respect to classical genetic algorithms. In the last problem, a cloning operator is incorporated in an evolutionary algorithm that builds a set of valid paths in a planar graph. It is demonstrated that cloning can enhance the ability of a genetic algorithm to explore the search space efficiently in some applications.