2012
DOI: 10.1109/tevc.2010.2098412
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Preference-Based Solution Selection Algorithm for Evolutionary Multiobjective Optimization

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Cited by 96 publications
(49 citation statements)
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“…To this end, statistical machine techniques, such as feature selection [36], principal component analysis (PCA) [37], [38], and maximum variance unfolding (MVU) [39] can be employed for objective reduction. In practice, users are often interested in only a part of the Pareto optimal solutions [40]. Therefore, if user preferences are available, preference-based approaches can be designed [41], [34], [42], [43].…”
mentioning
confidence: 99%
“…To this end, statistical machine techniques, such as feature selection [36], principal component analysis (PCA) [37], [38], and maximum variance unfolding (MVU) [39] can be employed for objective reduction. In practice, users are often interested in only a part of the Pareto optimal solutions [40]. Therefore, if user preferences are available, preference-based approaches can be designed [41], [34], [42], [43].…”
mentioning
confidence: 99%
“…There is no dispute over the point that there are reported applications in the literature, for example see Zhou et al (2011). However the scale of such problems pales in comparison to the size of problems that interior point methods can address, to the extent that Michalewicz (2012) million decision variables, constraints and objectives, while their convergence rate remains almost unaffected (Gondzio 2012), while to this day evolutionary algorithms mostly deal with problems with 2 or 3 objectives with relatively few addressing more, but no more than approximately 10, and with fewer than 100 decision variables (Kim et al 2012, Chiong and Kirley 2012, de Lange et al 2012, Wei et al 2012). Moreover there is not a single study available 1 that establishes a strong positive correlation between superior algorithm performance on test problems and real-world (and real-scale) problems.…”
Section: Discussionmentioning
confidence: 99%
“…The optimal solution corresponds to the one with the best partial evaluations. Despite not considering the criteria preferences, this kind of technique can be extended to include the degree of consideration for objectives [Kim et al, 2012]. MOP techniques, by expressing the objectives in functions where criteria are correlated, share the same issues as linear and integer programming approaches.…”
Section: One Of the Mathematical Optimization Methods Commonly Employmentioning
confidence: 99%