Optimization by Move-Class Deflation

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 ±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.