  A new approach to combinatorial optimization based on systematic move-class
deflation is proposed. The algorithm combines heuristics of genetic algorithms
and simulated annealing, and is mainly entropy-driven. It is tested on two
problems known to be NP hard, namely the problem of finding ground states of
the SK spin--glass and of the 3-$D$ $\pm J$ spin-glass. The algorithm is
sensitive to properties of phase spaces of complex systems other than those
explored by simulated annealing, and it may therefore also be used as a
diagnostic instrument. Moreover, dynamic freezing transitions, which are well
known to hamper the performance of simulated annealing in the large system
limit are not encountered by the present setup.
