| compute_R2HV | R Documentation | 
Compute the R2-HV from Shang et al.
compute_R2HV(dataPoints, reference, weights = NULL, nPoints = 100)
dataPoints | 
 The Points coordinate. Each column contains a single point (column major).  | 
reference | 
 The reference point for computing R2-mtch (similar as reference for HV)  | 
weights | 
 The weights/direction to be used to compute the achievement scalarization. Each column contains a single weight vector. If no weight is supplied, weights are generated using Sobol sequences.  | 
nPoints | 
 Used only when no weights are supplied. An input for the weight generator (sobol sequences). This defines how many points are created.  | 
The function return the powered R2-indicator of the set.
Ke Shang, Hisao Ishibuchi, Min-Ling Zhang, and Yiping Liu. 2018. A new R2 indicator for better hypervolume approximation. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18), Hernan Aguirre (Ed.). ACM, New York, NY, USA, 745-752. DOI: https://doi.org/10.1145/3205455.3205543
nPointToSample <- 100 nObjective <- 3 points <- matrix(runif(nPointToSample*nObjective), nrow = nObjective) # sample the points ranks <- nsga2R::fastNonDominatedSorting(t(points)) # non-dominated sorting points <- points[,ranks[[1]],drop=FALSE] # take only the non-dominated front nPoints <- ncol(points) # check how many points are on the non-dominated front reference <- rep(2,nObjective) compute_R2HV(points,reference)
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