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multi-objective
moocore:Core Mathematical Functions for Multi-Objective Optimization
Fast implementation of mathematical operations and performance metrics for multi-objective optimization, including filtering and ranking of dominated vectors according to Pareto optimality, computation of the empirical attainment function, V.G. da Fonseca, C.M. Fonseca, A.O. Hall (2001) <doi:10.1007/3-540-44719-9_15>, hypervolume metric, C.M. Fonseca, L. Paquete, M. López-Ibáñez (2006) <doi:10.1109/CEC.2006.1688440>, epsilon indicator, inverted generational distance, and Vorob'ev threshold, expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) <doi:10.1016/j.ejor.2014.07.032>, among others.
Maintained by Manuel López-Ibáñez. Last updated 11 days ago.
11 stars 6.28 score 7 scripts 4 dependents
mlopez-ibanez
eaf:Plots of the Empirical Attainment Function
Computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization. M. López-Ibáñez, L. Paquete, and T. Stützle (2010) <doi:10.1007/978-3-642-02538-9_9>.
Maintained by Manuel López-Ibáñez. Last updated 2 days ago.
eafeaf-differencesepsilonhypervolumeinverted-generational-distancemultiobjective-optimizationsummary-attainment-surfacesvisualizationgsl
18 stars 5.78 score 32 scripts 1 dependents