The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Diagnostic Assessment of Search Controls and Failure Modes in Many-Objective Evolutionary Optimization
Abstract
The growing popularity of multiobjective evolutionary algorithms (MOEAs) for solving many-objective problems warrants the careful investigation of their search controls and failure modes. This study contributes a new diagnostic assessment framework for rigorously evaluating the effectiveness, reliability, efficiency, and controllability of MOEAs as well as identifying their search controls and failure modes. The framework is demonstrated using the recently introduced Borg MOEA, [Formula: see text]-NSGA-II, [Formula: see text]-MOEA, IBEA, OMOPSO, GDE3, MOEA/D, SPEA2, and NSGA-II on 33 instances of 18 test problems from the DTLZ, WFG, and CEC 2009 test suites. The diagnostic framework exploits Sobol's variance decomposition to provide guidance on the algorithms’ non-separable, multi-parameter controls when performing a many-objective search. This study represents one of the most comprehensive empirical assessments of MOEAs ever completed.
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Cites
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Global Sensitivity Analysis. The Primer
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2007) · doi:10.1002/9780470725184 · Cited by 6,430
Techniques for highly multiobjective optimisation: some nondominated points are better than others
Corne, D. W., & Knowles, J. D. (2007) · Proceedings of the 9th annual conference on Genetic and evolutionary computation · doi:10.1145/1276958.1277115 · Cited by 273
Cited by
A Grid-Based Evolutionary Algorithm for Many-Objective Optimization
Yang, S., Li, M., Liu, X., & Zheng, J. (2013) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2012.2227145 · Cited by 893
Maier, H. R., Kapelan, Z., Kasprzyk, J., Kollat, J., Matott, L. S., Cunha, M. C., Dandy, G. C., Gibbs, M. S., Keedwell, E., Marchi, A., et al. (2014) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2014.09.013 · Cited by 657
Self-citationMany objective robust decision making for complex environmental systems undergoing change
Kasprzyk, J. R., Nataraj, S., Reed, P. M., & Lempert, R. J. (2013) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2012.12.007 · Cited by 508
Self-citationA survey on multi-objective evolutionary algorithms for many-objective problems
von Lücken, C., Barán, B., & Brizuela, C. (2014) · Computational Optimization and Applications · doi:10.1007/s10589-014-9644-1 · Cited by 297
Giuliani, M., Castelletti, A., Pianosi, F., Mason, E., & Reed, P. M. (2016) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0000570 · Cited by 257
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Metadata
- Authors
- Hadka, D., & Reed, P.
- Venue
- Evolutionary Computation
- Publisher
- MIT Press - Journals
- Published
- 2012-09
- Volume
- 20
- Issue
- 3
- Pages
- 423-452
- ISSN
- 1063-6560 (ISSN-L)
- 1530-9304
- OpenAlex
- W2165654898
- Cited by
- 213
- References
- 3
- FWCI
- 16.44
- Type
- journal article
- Language
- en
- Open access
- closed
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