A mixture experiments multi-objective hyper-heuristic

J A Vázquez-Rodríguez and S Petrovic
Additional contact information
J A Vázquez-Rodríguez: University of Nottingham, Nottinghamshire, UK
S Petrovic: University of Nottingham, Nottinghamshire, UK

Journal of the Operational Research Society, 2013, vol. 64, issue 11, 1664-1675

Abstract: This paper proposes a hyper-heuristic that combines genetic algorithm with mixture experiments to solve multi-objective optimisation problems. At every iteration, the proposed algorithm combines the selection criterion (rank indicator) of a number of well-established evolutionary algorithms including NSGA-II, SPEA2 and two versions of IBEA. Each indicator is called according to an associated probability calculated and updated during the search by means of mixture experiments. Mixture experiments are a particular type of experimental design suitable for the calibration of parameters that represent probabilities. Their main output is an explanatory model of algorithm performance as a function of its parameters. By finding the maximum (probability distribution) of the surface represented by this model, we also find a good algorithm parameterisation. The design of mixture experiments approach allowed the authors to identify and exploit synergies between the different rank indicators at the different stages of the search. This is demonstrated by our experimental results in which the proposed algorithm compared favourably against other well-established algorithms.

Date: 2013
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.palgrave-journals.com/jors/journal/v64/n11/pdf/jors2012125a.pdf Link to full text PDF (application/pdf)
http://www.palgrave-journals.com/jors/journal/v64/n11/full/jors2012125a.html Link to full text HTML (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:64:y:2013:i:11:p:1664-1675

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

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 ().