Stochastic GO algorithms

Eligius M. T. Hendrix () and Boglárka G.-Tóth ()
Additional contact information
Eligius M. T. Hendrix: Málaga University
Boglárka G.-Tóth: Budapest University of Technology and Economics

Chapter 7 in Introduction to Nonlinear and Global Optimization, 2010, pp 171-198 from Springer

Abstract: Abstract We consider stochastic methods as those algorithms that use (pseudo) random numbers in the generation of new trial points. The algorithms are used a lot in applications. Compared to deterministic methods they are often easy to implement. On the other hand, for many applied algorithms no theoretical background is given that the algorithm is effective and converges to a global optimum. Furthermore, we still do not know very well how fast the algorithms converge. For the effectiveness question, Törn and Žilinskas (1989) already stress that one should sample “everywhere dense”. This concept is as difficult with increasing dimension as doing a simple grid search. In Section 7.2 we describe some observations that have been found by several researchers on the question of increasing dimensions.

Keywords: Local Search; Minimum Point; Genetic Algorithm; Trial Point; Global Minimum Point (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spochp:978-0-387-88670-1_7

Ordering information: This item can be ordered from
http://www.springer.com/9780387886701

DOI: 10.1007/978-0-387-88670-1_7

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().