Description Usage Arguments Value Note References
View source: R/solver.eaxweighted.R
Inexact TSP solvers based on a genetic approach.
1 2 3 4 5 6 7 8 | ## S3 method for class 'eaxw'
run(solver, instance, max.trials = 1L, pop.size = 100L,
off.size = 30L, cutoff.time = 10L, opt.tour.length = NULL,
seed = as.integer(ceiling(runif(1L) * 2^15)), with.restarts = FALSE,
with.GPX = FALSE, fitness.type = "classic", snapshot.step = 0L,
full.matrix = FALSE, verbose = FALSE, log.trajectory = TRUE,
work.dir = NULL, output.files.prefix = NULL,
keep.output.files = FALSE, init.pop = NULL, ...)
|
solver |
[ |
instance |
[ |
max.trials |
[ |
pop.size |
[ |
off.size |
[ |
cutoff.time |
[ |
opt.tour.length |
[ |
seed |
[ |
with.restarts |
[ |
with.GPX |
[ |
fitness.type |
[ |
snapshot.step |
[ |
full.matrix |
[ |
verbose |
[ |
log.trajectory |
[ |
work.dir |
[ |
output.files.prefix |
[ |
keep.output.files |
[ |
init.pop |
[
Default is |
... |
[any] |
[TSPSolverResult
]
This solver requires integer inter-city distances.
Nagata, Y. and Kobayashi, S. (2013). A powerful genetic algorithm using edge assembly crossover for the travelling salesman problem. INFORMS Journal on Computing, 25(2):346-363.
Nagata, Y. and Kobayashi, S. (1997). Edge assembly crossover: A high-power genetic algorithm for the travelling salesman problem. In Baeck, T., editor, Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA97), pages 450-457, San Francisco, CA. Morgan Kaufmann.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.