Description Usage Arguments Value Note Examples
View source: R/computeCumulatedPathLengths.R
Given a grid of points in the search space, along with their corresponding multi-objective gradients, this function will compute (for each point of the grid) the length of the cumulated path from a point towards its attracting local efficient point.
1 2 | computeCumulatedPathLengths(centers, gradients,
prec.vector.length = 0.001, prec.norm = 1e-06, check.data = TRUE)
|
centers |
[ |
gradients |
[ |
prec.vector.length |
[ |
prec.norm |
[ |
check.data |
[ |
[data.frame
]
Returns a data.frame
, which appends the cumulated path lengths to the points
provided by centers
.
ATTENTION: Only turn off the sanity checks (check.data = FALSE
),
if you can ensure that all input parameters are provided in the correct format.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | # Define two single-objective test problems:
fn1 = function(x) sum((x - c(0.2, 1))^2)
fn2 = function(x) sum((x - c(0.5, 0.5))^2)
# Define a grid of points:
points = as.matrix(expand.grid(x1 = seq(0, 0.7, 0.005), x2 = seq(0, 1.25, 0.005)))
# Compute the corresponding gradients:
gradients = computeGradientField(points, fn1, fn2)
# Now, compute the cumulated path lengths:
x = computeCumulatedPathLengths(points, gradients)
# Finally, we can visualize the resulting multi-objective "landscape":
ggplotHeatmap(x, hide.legend = TRUE)
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