A hybrid genetic algorithmic approach to the maximally diverse grouping problem

Z P Fan, Y Chen, J Ma and S Zeng
Additional contact information
Z P Fan: Northeastern University
Y Chen: Shanghai University of Finance & Economics
J Ma: City University of Hong Kong
S Zeng: University of Arizona

Journal of the Operational Research Society, 2011, vol. 62, issue 1, 92-99

Abstract: Abstract The maximally diverse grouping problem (MDGP) is a NP-complete problem. For such NP-complete problems, heuristics play a major role in searching for solutions. Most of the heuristics for MDGP focus on the equal group-size situation. In this paper, we develop a genetic algorithm (GA)-based hybrid heuristic to solve this problem considering not only the equal group-size situation but also the different group-size situation. The performance of the algorithm is compared with the established Lotfi–Cerveny–Weitz algorithm and the non-hybrid GA. Computational experience indicates that the proposed GA-based hybrid algorithm is a good tool for solving MDGP. Moreover, it can be easily modified to solve other equivalent problems.

Keywords: genetic algorithm; maximally diverse grouping problem; local neighbourhood search (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://link.springer.com/10.1057/jors.2009.168 Abstract (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:pal:jorsoc:v:62:y:2011:i:1:d:10.1057_jors.2009.168

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/41274

DOI: 10.1057/jors.2009.168

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald and Jonathan Crook

More articles in Journal of the Operational Research Society from Palgrave Macmillan, The OR Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().