Optimization by Move--Class Deflation
Statistical Mechanics
2007-05-23 v1 Disordered Systems and Neural Networks
Abstract
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- 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.
Cite
@article{arxiv.cond-mat/9805137,
title = {Optimization by Move--Class Deflation},
author = {Reimer Kuehn and Yu-Cheng Lin and Gerhard Poeppel},
journal= {arXiv preprint arXiv:cond-mat/9805137},
year = {2007}
}
Comments
16 pages, 10 eps figures