A Probabilistic Hybrid Differential Evolution Algorithm

Montaz M. Ali ()
Additional contact information
Montaz M. Ali: Witwatersrand University

A chapter in Models and Algorithms for Global Optimization, 2007, pp 173-184 from Springer

Abstract: Summary In this chapter we propose a hybrid point generation scheme in the differential evolution (DE) algorithm. In particular, we propose a DE algorithm that uses a probabilistic combination of the point generation by the β-distribution and the point generation by mutation. Numerical results suggest that the resulting algorithm is superior to the original version both in terms of the number of function evaluations and cpu times.

Keywords: Global optimization; population set; β-distribution; continuous variable; probabilistic adaption (search for similar items in EconPapers)
Date: 2007
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:978-0-387-36721-7_11

Ordering information: This item can be ordered from
http://www.springer.com/9780387367217

DOI: 10.1007/978-0-387-36721-7_11

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 ().