mutSubgraphMST selects a random edge e = (u, v) and traverses
the tree starting form u and v respectively until a connected subtree of at most
sigma edges is selected. Then the subtree is replaced with the optimal spanning subtree
regarding one of the objectives with equal probability.
mutSubgraphMST(
ind,
sigma = floor(ncol(ind)/2),
scalarize = FALSE,
instance = NULL
)[matrix(2, m)] Mutated edge list.
[matrix(2, m)]
Matrix of edges (each column is one edge).
[integer()]
Upper bound for the size of the selected subtree.
[logical(1)]
Should a scalarized version of the the subproblem be solved?
If TRUE, a random weight \(\lambda \in [0,1]\) is sampled
from a U[0, 1]-distribution. Next, a weighted sum
scalarization \(\lambda \cdot c_1 + (1 - \lambda) \cdot c_2\)
of the subproblem is solved.
Default is FALSE, i.e., the single-objective subproblem is
solved. The objective to optimize for is sampled with equal probability.
[grapherator]
Multi-objective graph.
Evolutionary multi-objective algorithm mcMSTEmoaBG
Other mcMST EMOA mutators:
mutEdgeExchange(),
mutKEdgeExchange(),
mutSubforestMST(),
mutUniformPruefer()