Dr Noura Al Moubayed noura.al-moubayed@durham.ac.uk
Associate Professor
Natalio Krasnogor
Editor
Clustering-based Leaders Selection (CLS) is a novel leaders selection technique in multi-objective evolutionary algorithms. Clustering is applied on both the objective and solution spaces whereby each individual is assigned to two clusters; one in the objective space and the other in the solution space. Mapping between clusters in both spaces is then applied to recognize regions with potentially better solutions. A leaders archive is used where a representative of each cluster in the objective and solution spaces is stored. The results of applying CLS integrated with NSGAII on seven standard multi-objective problems, show that clustering based leaders selection NSGAII (NSGAII/C) is highly competitive comparing with the original algorithm.
Al Moubayed, N., Petrovski, A., & McCall, J. (2011). Clustering based leaders' selection in multi-objective evolutionary algorithms. In N. Krasnogor (Ed.), . https://doi.org/10.1145/2001858.2001913
Presentation Conference Type | Conference Paper (Published) |
---|---|
Conference Name | Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11 |
Start Date | Jul 12, 2011 |
End Date | Jul 16, 2011 |
Online Publication Date | Jul 12, 2011 |
Publication Date | 2011-07 |
Deposit Date | Jan 26, 2016 |
Publisher | Association for Computing Machinery (ACM) |
Pages | 95-96 |
ISBN | 9781450306904 |
DOI | https://doi.org/10.1145/2001858.2001913 |
Keywords | Leaders Selection, Multi-Objective Optimization, Clustering, Evolutionary Algorithm, Density Based Spatial Clustering, Principal Component Analysis. |
Public URL | https://durham-repository.worktribe.com/output/1151590 |
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