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Particle algorithms for optimization on binary spaces

Authors
  • Schäfer, Christian
Type
Preprint
Publication Date
Apr 06, 2012
Submission Date
Nov 02, 2011
Identifiers
arXiv ID: 1111.0574
Source
arXiv
License
Yellow
External links

Abstract

We discuss a unified approach to stochastic optimization of pseudo-Boolean objective functions based on particle methods, including the cross-entropy method and simulated annealing as special cases. We point out the need for auxiliary sampling distributions, that is parametric families on binary spaces, which are able to reproduce complex dependency structures, and illustrate their usefulness in our numerical experiments. We provide numerical evidence that particle-driven optimization algorithms based on parametric families yield superior results on strongly multi-modal optimization problems while local search heuristics outperform them on easier problems.

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