mutForestMST drops k edges randomly. In consequence the
tree is decomposed into k+1 subtrees (forest). Now the operator reconnects the
subtrees by constructing a minimum spanning tree between the components.
mutSubforestMST(ind, sigma = ncol(ind), 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 number of dropped edges.
[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(),
mutSubgraphMST(),
mutUniformPruefer()