run.eax: Solver: EAX

In jakobbossek/salesperson: Computation of Instance Features and R Interface to the State-of-the-Art Exact and Inexact Solvers for the Traveling Salesperson Problem

Description

Inexact TSP solvers based on a genetic approach.

Usage

## S3 method for class 'eax'
run(solver, instance, max.trials = 1L, pop.size = 100L,
  off.size = 30L, cutoff.time = 10, opt.tour.length = NULL,
  seed = as.integer(ceiling(runif(1L) * 2^15)), with.restarts = FALSE,
  with.GPX = FALSE, 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, ...)

Arguments

solver	[TSPSolver] TSPSolver object.
instance	[character(1) | Network] Path to TSP instance in TSPlib format or Network object.
max.trials	[integer(1)] Number of independent runs. At the moment this is fixed to 1.
pop.size	[integer(1)] Population size. Default is 100.
off.size	[integer(1)] Number of offspring generated in each generation. Default is 30.
cutoff.time	[numeric(1)] Maximal running time in seconds. Default is 10.
opt.tour.length	[integer(1) | NULL] Length of an optimal TSP tour. This is only relevant for benchmarking. Keep in mind, that in case of a given optimal tour length most internal stopping criteria of EAX are deacitvated. The algorithm stops if an optimal tour is found or the cutoff time is reached.
seed	[integer(1)] Seed for the random numbers generator. Defaults to a random integer between 1 and 2^15.
with.restarts	[logical(1)] Should EAX restart if a plateau is reached? Default is FALSE.
with.GPX	[logical(1)] Activate GPX2 crossover? Default is FALSE.
snapshot.step	[integer(1)] Possibility to log the entire population each snapshot.step times. Default is 0, i.e., do not log at all.
full.matrix	[logical(1)] Only relevant if instance is a Network. If TRUE, the network is exported to TSPlib format with explicit edge weight definition. Default is FALSE.
verbose	[logical(1)] Should the output of the solver be printed? Default is FALSE.
log.trajectory	[logical(1)] Should the optimization progress be logged? Default is FALSE.
work.dir	[character(1)] Path to working directory of the solver. Defaults to temporary directory via tempfile().
output.files.prefix	[character(1)] Prefix for file names used by solver for storing output. Default is a randomly generated temporary prefix.
keep.output.files	[logical(1)] Flag indicating whether output files shall be kept or deleted. Default is FALSE, i.e., output files are processed and output is loaded into R object. Afterwards the output files are deleted. This flag is particularly useful in large benchmark studies if work.dir and output.files.prefix is set.
init.pop	[list] List of lists. Each sublist needs to contains three components: n [integer(1)] Number of nodes of the TSP problem. tour [integer(n)] The actual tour. tour.length [integer(1)] Length of the tour. Default is NULL, i.e., the initial population is generated randomly and 2-Opt is applied to each solution before the evolutionary loop starts.
...	[any] Not used at the moment.

Value

[TSPSolverResult]

Note

This solver requires integer inter-city distances.

References

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.

jakobbossek/salesperson documentation built on Dec. 11, 2021, 4:54 a.m.