Optimization by Move--Class Deflation
| dc.creator | Kuehn, Reimer | |
| dc.creator | Lin, Yu-Cheng | |
| dc.creator | Poeppel, Gerhard | |
| dc.date | 1998-05-12 | |
| dc.date.accessioned | 2026-07-07T03:10:32Z | |
| dc.date.available | 2026-07-07T03:10:32Z | |
| dc.description | 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. | |
| dc.description | 16 pages, 10 eps figures | |
| dc.identifier | https://arxiv.org/abs/cond-mat/9805137 | |
| dc.identifier | http://arxiv.org/abs/cond-mat/9805137 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/28268 | |
| dc.subject | Statistical Mechanics | |
| dc.subject | Disordered Systems and Neural Networks | |
| dc.title | Optimization by Move--Class Deflation | |
| dc.type | text |