  The most efficient MC weights for the calculation of physical, canonical
expectation values are not necessarily those of the canonical ensemble. The use
of suitably generalized ensembles can lead to a much faster convergence of the
simulation. Although not realized by nature, these ensembles can be implemented
on computers. In recent years generalized ensembles have in particular been
studied for the simulation of complex systems. For these systems it is typical
that conflicting constraints lead to free energy barriers, which fragment the
configuration space. Examples of major interest are spin glasses and proteins.
In my overview I first comment on the strengths and weaknesses of a few major
approaches, multicanonical simulations, transition variable methods, and
parallel tempering. Subsequently, two applications are presented: a new
analysis of the Parisi overlap distribution for the 3d Edwards-Anderson Ising
spin glass and the helix-coil transition of amino-acid homo-oligomers.
