  We present a genetic algorithm (GA) for structural search that combines the
speed of structure exploration by classical potentials with the accuracy of
density functional theory (DFT) calculations in an adaptive and iterative way.
This strategy increases the efficiency of the DFT-based GA by several orders of
magnitude. This gain allows considerable increase in size and complexity of
systems that can be studied by first principles. The method's performance is
illustrated by successful structure identifications of complex binary and
ternary inter-metallic compounds with 36 and 54 atoms per cell, respectively.
The discovery of a multi-TPa Mg-silicate phase with unit cell containing up to
56 atoms is also reported. Such phase is likely to be an essential component of
terrestrial exoplanetary mantles.
