build: Instance generation working horse.
In jakobbossek/tspgen: Problem Generator for Traveling Salesperson Problems

Description Usage Arguments Value Note Examples

This function expects as primary parameters the desired instance size n, a number of iteration iters to perform and a collection of mutation operators collection. The generation process is sequential:

Place n points uniformly at random in [0,1]^2. We shall denote this set as P in the following..
For the desired number of iterations iters repeat the following process: select a mutation operator m from the collection at random (according to the probability distribution stored in collection and set P = m(P).

1 2	build(n, iters = 10L, collection, return.all = FALSE, upper = 1, bound.handling = "uniform")

`n`	[`integer(1)`] Desired instance size, i.e., the number of nodes.
`iters`	[`integer(1)`] Number of iterations to perform. Default is 10.
`collection`	[`tspgen_collection`] Collection of mutation operators.
`return.all`	[`logical(1)`] Shall all interim instances be stored and returned? Default is `FALSE`.
`upper`	[`numeric(1)`] Instance generation takes place in [0,1]^2. Use `upper` to upscale the boundaries, i.e., place nodes in [0, upper]^2. Default is 1.
`bound.handling`	[`character(1)`] Occasionally during instance generation points are moved outside the boundaries of the point space. The parameter `bound.handling` determines how to deal with these points. Option “uniform” places outliers uniform at random within . the boundaries while option “boundary” places them on the corresponding violates boundary/boundaries. Default is “uniform”.

Either a netgen Network if return.all = FALSE, otherwise a list of netgen networks of length iters + 1.

If setProbabilities was not called on the collection the algorithm falls back to the uniform distribution, i.e., each mutation operator is selected with equal probability for application in each iteration.

# set up a set of mutation operators
collection = init()
collection = addMutator(collection, "doUniformMutation", pm = 0.3)
collection = addMutator(collection, "doExplosionMutation", min.eps = 0.2, max.eps = 0.4)
collection = addMutator(collection, "doImplosionMutation", min.eps = 0.2, max.eps = 0.4)
collection = addMutator(collection, "doAxisProjectionMutation")

# specify probability distribution.
collection = setProbabilities(collection, probs = c(0.1, 0.6, 0.2, 0.1))

x = build(n = 50, iters = 10, collection = collection)
x = build(n = 100, iters = 50, collection = collection, return.all = TRUE, bound.handling = "boundary")