Interactive Evolutionary Multiobjective Optimization Using Robust Ordinal Regression

Branke, Jürgen; Greco, Salvatore; Słowiński, Roman; Zielniewicz, Piotr

doi:10.1007/978-3-642-01020-0_43

Cited by 85 publications

(87 citation statements)

References 16 publications

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“…Repeated optimizations using standard packages for constrained optimization (here the Nonlinear Interior point Trust Region Optimization package, ref. 32) prove quite practical, although advanced multiobjective techniques (20,21) could be applied. Fig.…”

Section: Multiobjective Optimizationmentioning

confidence: 99%

Considerations for parameter optimization and sensitivity in climate models

Neelin

Bracco

Luo

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

108

120

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Climate models exhibit high sensitivity in some respects, such as for differences in predicted precipitation changes under global warming. Despite successful large-scale simulations, regional climatology features prove difficult to constrain toward observations, with challenges including high-dimensionality, computationally expensive simulations, and ambiguity in the choice of objective function. In an atmospheric General Circulation Model forced by observed sea surface temperature or coupled to a mixed-layer ocean, many climatic variables yield rms-error objective functions that vary smoothly through the feasible parameter range. This smoothness occurs despite nonlinearity strong enough to reverse the curvature of the objective function in some parameters, and to imply limitations on multimodel ensemble means as an estimator of global warming precipitation changes. Low-order polynomial fits to the model output spatial fields as a function of parameter (quadratic in model field, fourth-order in objective function) yield surprisingly successful metamodels for many quantities and facilitate a multiobjective optimization approach. Tradeoffs arise as optima for different variables occur at different parameter values, but with agreement in certain directions. Optima often occur at the limit of the feasible parameter range, identifying key parameterization aspects warranting attention-here the interaction of convection with free tropospheric water vapor. Analytic results for spatial fields of leading contributions to the optimization help to visualize tradeoffs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional error under minimization of global objective functions. The approach is sufficiently simple to guide parameter choices and to aid intercomparison of sensitivity properties among climate models.climate model optimization | metamodeling | precipitation bias and sensitivity I nterest in systematic parameter sensitivity and optimization has been developing both in the context of global average climate sensitivity associated with increased greenhouse gases and the effort to improve the model climatology (1-7). Some of this work has focused on variations with parameter of a climate sensitivity defined by the change of global average surface temperature under doubled CO 2 , some on optimizing the simulation of current climate features by tuning parameter values. Here we examine related questions in sensitivity and optimization, with a particular interest in precipitation. Despite capturing large-scale features, simulations of precipitation in current climate are subject to considerable regional-scale bias (8-12). A common experience is that the simulated climatology exhibits high sensitivity to parameterization changes in certain respects but nonetheless proves difficult to constrain toward observations. At the same time, predicted changes in seasonal precipitation under global warming exhibit striking disagreement among models (13-15). This manifestation of sensitivi...

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Section: Multiobjective Optimizationmentioning

confidence: 99%

Considerations for parameter optimization and sensitivity in climate models

Neelin

Bracco

Luo

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

108

120

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“…The C-lower approximations and the C-upper approximations of S and S c , obtained by means of multigraded dominance relations, are: (1,4), (1,5), (1,6), (1,8), (3,2), (3,4), (3,5), (3,6), (3,8), (7,2), (7,4), (7,5), (7,6), (7,8) C(S c ) = (2,1), (2,7), (4,1), (4,3), (4,7), (5,1), (5,3), (5,7), (6,1), (6,3), (6,7), (8,1), (8,7) All the remaining 36 pairs of reference objects belong to the C-boundaries of S and S c , i.e. Bn C (S) = Bn C (S c ).…”

Section: Moreover R E D S Pc T = C O R E S Pcmentioning

confidence: 99%

“…1 y and x P ≥1 2 y, then x Sy; (1,6), (3,6), (7,6) If x P ≥1 2 y and x P ≥0 3 y, then x Sy; (1,2), (1,6), (1,8), (3,2), (3,6), (3,8), (7,2), (7,6), (7,8) If If x P ≥0 2 y and x P ≥1 3 y, then x Sy; (1,4), (1,5), (3,4), (3,5), (7,4), (7,5) If y and x P ≤0 3 y, then x S c y; (2,1),(2,7),(6,1), (6,3), (6,7), (8,1), (8,7) Moreover, it is possible to induce five minimal D ≥≤ -decision rules from the boundary of approximation of S and S c :…”

Section: If X P ≥1mentioning

confidence: 99%

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Rough set and rule-based multicriteria decision aiding

2012

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ABSTRACT. The aim of multicriteria decision aiding is to give the decision maker a recommendation concerning a set of objects evaluated from multiple points of view called criteria. Since a rational decision maker acts with respect to his/her value system, in order to recommend the most-preferred decision, one must identify decision maker's preferences. In this paper, we focus on preference discovery from data concerning some past decisions of the decision maker. We consider the preference model in the form of a set of "if..., then..." decision rules discovered from the data by inductive learning. To structure the data prior to induction of rules, we use the Dominance-based Rough Set Approach (DRSA). DRSA is a methodology for reasoning about data, which handles ordinal evaluations of objects on considered criteria and monotonic relationships between these evaluations and the decision. We review applications of DRSA to a large variety of multicriteria decision problems.

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“…Preference information can be elicited in various forms, including, but not limited to, a reference point (desired solution) (Fonseca & Fleming, 1998), pairwise comparisons of solutions (Branke et al, 2009), or maximum/minimum trade-offs (Branke et al, 2001). Recent surveys on integrating user preferences in EMO can be found in Branke (2008); Jaszkiewicz & Branke (2008).…”

Section: Interactive Emomentioning

confidence: 99%

An Overview on Evolutionary Algorithms

Data Mining Using Grammar Based Genetic Programming and Applications

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Interactive Evolutionary Multiobjective Optimization Using Robust Ordinal Regression

Cited by 85 publications

References 16 publications

Considerations for parameter optimization and sensitivity in climate models

Considerations for parameter optimization and sensitivity in climate models

Rough set and rule-based multicriteria decision aiding

An Overview on Evolutionary Algorithms

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