On stochastic global optimization of one-dimensional functions

Kay Hamacher

Physica A: Statistical Mechanics and its Applications, 2005, vol. 354, issue C, 547-557

Abstract: We consider the applicability of stochastic global optimization algorithms on test-functions whose domain of definition is a simply-connected and finite interval of real numbers. We argue on the basis of theoretical reflections of statistical physics (namely random-walk) and computer simulations that there is a decisive difference between test-problems in one and multiple dimensions pointing to the necessity to only consider test-functions in higher dimensions. We argue that only test-problems in two or more dimensions provide for the possibility to discriminate the efficiency of stochastic global optimization algorithms with respect to the complexity of the underlying physical system at all.

Keywords: Global optimization; Potential energy surface; Monte Carlo; Diffusive process (search for similar items in EconPapers)
Date: 2005
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437105001834
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:354:y:2005:i:c:p:547-557

DOI: 10.1016/j.physa.2005.02.028

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().