compute_R2HVC: Modified tchebyscheff R2-indicator contribution designed to... In MaOEA: Many Objective Evolutionary Algorithm

Description

Compute the R2-HVC from Shang et al.

Usage

 ```1 2 3 4 5 6 7 8``` ```compute_R2HVC( dataPoints, reference, weights = NULL, alpha = 1, nWeight = 300, indexOfInterest = 1:ncol(dataPoints) ) ```

Arguments

 `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 `alpha` Power factor on the gmtch and g*2tch utility functions. `nWeight` Used only when no weights are supplied. The number of weights generated by sobol sequence. `indexOfInterest` individuals to be evaluated. The R2 values will only be reported/returned for these individuals.

Value

The function return R2-indicator contribution of each point.

References

K. Shang, H. Ishibuchi and X. Ni, "R2-based Hypervolume Contribution Approximation," in IEEE Transactions on Evolutionary Computation. doi: 10.1109/TEVC.2019.2909271

Examples

 ```1 2 3 4 5 6 7 8 9``` ```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_R2HVC(points,reference) ```

MaOEA documentation built on Aug. 31, 2020, 5:07 p.m.