Core Mathematical Functions for Multi-Objective Optimization
Convert input to a matrix with "double" storage mode (`base::storage...
Convert a list of attainment surfaces to a single EAF data.frame.
Interactively choose according to empirical attainment function differ...
Same as eaf() but performs no checks and does not transform the inpu...
Same as eafdiff() but performs no checks and does not transform the ...
Convert an EAF data frame to a list of data frames, where each element...
Exact computation of the Empirical Attainment Function (EAF)
Compute empirical attainment function differences
Epsilon metric
Generate a random set of mutually nondominated points
Approximate the hypervolume indicator.
Hypervolume contribution of a set of points
Hypervolume metric
Inverted Generational Distance (IGD and IGD+) and Averaged Hausdorff D...
Identify largest EAF differences
moocore: Core Mathematical Functions for Multi-Objective Optimization
Identify, remove and rank dominated points according to Pareto optimal...
Normalise points
Combine datasets x and y by row taking care of making all sets uni...
Read several data sets
Transform matrix according to maximise parameter
Vorob'ev threshold, expectation and deviation
Approximation of the (weighted) hypervolume by Monte-Carlo sampling (2...
Compute (total) weighted hypervolume given a set of rectangles
Write data sets
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.
Useful links