  The growth of evolutionary computing (EC) methods in the exploration of
complex potential energy landscapes of atomic and molecular clusters, as well
as crystals over the last decade or so is reviewed. The trend of growth
indicates that pure as well as hybrid evolutionary computing techniques in
conjunction of DFT has been emerging as a powerful tool, although work on
molecular clusters has been rather limited so far. Some attempts to solve the
atomic/molecular Schrodinger Equation (SE) directly by genetic algorithms (GA)
are available in literature. At the Born-Oppenheimer level of approximation
GA-density methods appear to be a viable tool which could be more extensively
explored in the coming years, specially in the context of designing molecules
and materials with targeted properties.

Materials Science;Neural and Evolutionary Computing;Chemical Physics