Order statistics and region-based evolutionary computation

S. Puechmorel () and D. Delahaye

Journal of Global Optimization, 2014, vol. 59, issue 1, 107-130

Abstract: Trust region algorithms are well known in the field of local continuous optimization. They proceed by maintaining a confidence region in which a simple, most often quadratic, model is substituted to the criterion to be minimized. The minimum of the model in the trust region becomes the next starting point of the algorithm and, depending on the amount of progress made during this step, the confidence region is expanded, contracted or kept unchanged. In the field of global optimization, interval programming may be thought as a kind of confidence region approach, with a binary confidence level: the region is guaranteed to contain the optimum or guaranteed to not contain it. A probabilistic version, known as branch and probability bound, is based on an approximate probability that a region of the search space contains the optimum, and has a confidence level in the interval [0,1]. The method introduced in this paper is an application of the trust region approach within the framework of evolutionary algorithms. Regions of the search space are endowed with a prospectiveness criterion obtained from random sampling possibly coupled with a local continuous algorithm. The regions are considered as individuals for an evolutionary algorithm with mutation and crossover operators based on a transformation group. The performance of the algorithm on some standard benchmark functions is presented. Copyright Springer Science+Business Media New York 2014

Keywords: Extreme values; Region based algorithm; Trust region; Evolutionary algorithm (search for similar items in EconPapers)
Date: 2014
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/s10898-013-0079-5 (text/html)
Access to full text is restricted to subscribers.

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:spr:jglopt:v:59:y:2014:i:1:p:107-130

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/10898

DOI: 10.1007/s10898-013-0079-5

Access Statistics for this article

Journal of Global Optimization is currently edited by Sergiy Butenko

More articles in Journal of Global Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().