Erratum : A hybrid genetic algorithmic approach to the maximally diverse grouping problem (Journal of the Operational Research Society (2010) DOI 10.1057/jors.2009.168)
Research output: Journal Publications and Reviews › Erratum
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 1423-1430 |
| Journal / Publication | Journal of the Operational Research Society |
| Volume | 62 |
| Issue number | 7 |
| Publication status | Published - 2011 |
Link(s)
Abstract
Corrections to: Journal of the Operational Research Society (2010). doi:10.1057/jors.2009.168; published online 6 January 2010
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.
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.
Research Area(s)
- genetic algorithm, local neighbourhood search, maximally diverse grouping problem
Bibliographic Note
Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s)
Citation Format(s)
Erratum: A hybrid genetic algorithmic approach to the maximally diverse grouping problem (Journal of the Operational Research Society (2010) DOI 10.1057/jors.2009.168). / Fan, Z. P.; Chen, Y.; Ma, J. et al.
In: Journal of the Operational Research Society, Vol. 62, No. 7, 2011, p. 1423-1430.
In: Journal of the Operational Research Society, Vol. 62, No. 7, 2011, p. 1423-1430.
Research output: Journal Publications and Reviews › Erratum
