DEoptim.control {DEoptim} | R Documentation |
Allow the user to set some characteristics of the
Differential Evolution optimization algorithm implemented
in DEoptim
.
DEoptim.control(VTR = -Inf, strategy = 2, bs = TRUE, NP = 50, itermax = 200, CR = 0.5, F = 0.8, trace = TRUE, initialpop = NULL, storepopfrom = itermax + 1, storepopfreq = 1)
VTR |
The value to be reached. The optimization process
will stop if either the maximum number of iterations itermax
is reached or the best parameter vector bestmem has found a value
fn(bestmem) <= VTR . Default to -Inf . |
strategy |
Defines the Differential Evolution
strategy used in the optimization procedure:1 : DE / rand / 1 / bin (classical strategy)2 : DE / local-to-best / 1 / bin 3 : DE / best / 1 / bin with jitter4 : DE / rand / 1 / bin with per-vector-dither5 : DE / rand / 1 / bin with per-generation-ditherany value not above: variation to DE / rand / 1 / bin: either-or-algorithm. |
bs |
Enables best of parent and child selection if TRUE or
DEoptim standard trial vs. target selection if FALSE .
Default is TRUE . |
NP |
Number of population members. Default to 50 .
Cannot be set larger than 2000. |
itermax |
The maximum iteration (population generation) allowed.
Default is 200 . |
CR |
Crossover probability from interval [0,1]. Default
to 0.5 . |
F |
Stepsize from interval [0,2]. Default to 0.8 . |
trace |
Printing of progress occurs? Default to TRUE . |
initialpop |
An initial population used as a starting
population in the optimization procedure. Maybe useful to speed up
the convergence. Default to NULL . |
storepopfrom |
From which population should the following
intermediate populations be stored in memory. Default to
itermax+1 , i.e., no intermediate population is stored. |
storepopfreq |
The frequency of populations'
storage. Default to 1 , i.e. every intermediate population
is memorized. |
A list
with components:
VTR |
|
strategy |
|
bs |
|
NP |
|
itermax |
|
CR |
|
F |
|
trace |
with meanings as explained under ‘Arguments’.
Please cite the package in publications. Use citation("DEoptim")
.
David Ardia david.ardia@unifr.ch and Katharine Mullen katharine.mullen@nist.gov.
Differential Evolution homepage: http://www.icsi.berkeley.edu/~storn/code.html
Price, K.V., Storn, R.M., Lampinen J.A. (2005). Differential Evolution - A Practical Approach to Global Optimization. Springer-Verlag. ISBN 3540209506.
DEoptim
and DEoptim-methods
.
DEoptim.control(NP = 20) DEoptim.control(NP = 20, itermax = 100, trace = FALSE)