DEoptim.control  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 = FALSE, NP = NA, itermax = 200, CR = 0.5, F = 0.8, trace = TRUE, initialpop = NULL, storepopfrom = itermax + 1, storepopfreq = 1, p = 0.2, c = 0, reltol, steptol, parallelType = c("none", "auto", "parallel", "foreach"), cluster = NULL, packages = c(), parVar = c(), foreachArgs = list(), parallelArgs = NULL)
VTR 
the value to be reached. The optimization process
will stop if either the maximum number of iterations 
strategy 
defines the Differential Evolution
strategy used in the optimization procedure: 
bs 
if 
NP 
number of population members. Defaults to 
itermax 
the maximum iteration (population generation) allowed.
Default is 
CR 
crossover probability from interval [0,1]. Default
to 
F 
differential weighting factor from interval [0,2]. Default
to 
trace 
Positive integer or logical value indicating whether
printing of progress occurs at each iteration. The default value is

initialpop 
an initial population used as a starting
population in the optimization procedure. May be useful to speed up
the convergence. Default to 
storepopfrom 
from which generation should the following
intermediate populations be stored in memory. Default to

storepopfreq 
the frequency with which populations are stored.
Default to 
p 
when 
c 

reltol 
relative convergence tolerance. The algorithm stops if
it is unable to reduce the value by a factor of 
steptol 
see 
parallelType 
Defines the type of parallelization to employ, if
any.

cluster 
Existing parallel cluster object. If provided, overrides
+ specified 
packages 
Used if 
parVar 
Used if 
foreachArgs 
A list of named arguments for the 
parallelArgs 
A list of named arguments for the parallel engine.
For package foreach, the argument 
This defines the Differential Evolution strategy used in the optimization procedure, described below in the terms used by Price et al. (2006); see also Mullen et al. (2009) for details.
strategy = 1
: DE / rand / 1 / bin.
This strategy is the classical approach for DE, and is described in DEoptim
.
strategy = 2
: DE / localtobest / 1 / bin.
In place of the classical DE mutation the expression
v_i,g = old_i,g + (best_g  old_i,g) + x_r0,g + F * (x_r1,g  x_r2,g)
is used, where old_i,g and best_g are the ith member and best member, respectively, of the previous population. This strategy is currently used by default.
strategy = 3
: DE / best / 1 / bin with jitter.
In place of the classical DE mutation the expression
v_i,g = best_g + jitter + F * (x_r1,g  x_r2,g)
is used, where jitter is defined as 0.0001 * rand
+ F.
strategy = 4
: DE / rand / 1 / bin with per vector dither.
In place of the classical DE mutation the expression
v_i,g = x_r0,g + dither * (x_r1,g  x_r2,g)
is used, where dither is calculated as F + \code{rand} * (1  F).
strategy = 5
: DE / rand / 1 / bin with per generation dither.
The strategy described for 4
is used, but dither
is only determined once pergeneration.
strategy = 6
: DE / currenttopbest / 1.
The top (100*p) percent best solutions are used in the mutation,
where p is defined in (0,1].
any value not above: variation to DE / rand / 1 / bin: eitheror algorithm.
In the case that rand
< 0.5, the classical strategy strategy = 1
is used.
Otherwise, the expression
v_i,g = x_r0,g + 0.5 * (F + 1) * (x_r1,g + x_r2,g  2 * x_r0,g)
is used.
Several conditions can cause the optimization process to stop:
if the best parameter vector (bestmem
) produces a value
less than or equal to VTR
(i.e. fn(bestmem) <= VTR
), or
if the maximum number of iterations is reached (itermax
), or
if a number (steptol
) of consecutive iterations are unable
to reduce the best function value by a certain amount (reltol *
(abs(val) + reltol)
). 100*reltol
is approximately the percent
change of the objective value required to consider the parameter set
an improvement over the current best member.
Zhang and Sanderson (2009) define several extensions to the DE algorithm,
including strategy 6, DE/currenttopbest/1. They also define a selfadaptive
mechanism for the other control parameters. This selfadaptation will speed
convergence on many problems, and is defined by the control parameter c
.
If c
is nonzero, crossover and mutation will be adapted by the algorithm.
Values in the range of c=.05
to c=.5
appear to work best for most
problems, though the adaptive algorithm is robust to a wide range of c
.
The default value of control
is the return value of
DEoptim.control()
, which is a list (and a member of the
S3
class
DEoptim.control
) with the above elements.
Further details and examples of the R package DEoptim can be found
in Mullen et al. (2011) and Ardia et al. (2011a, 2011b) or look at the
package's vignette by typing vignette("DEoptim")
. Also, an illustration of
the package usage for a highdimensional nonlinear portfolio optimization problem
is available by typing vignette("DEoptimPortfolioOptimization")
.
Please cite the package in publications. Use citation("DEoptim")
.
David Ardia, Katharine Mullen mullenkate@gmail.com, Brian Peterson and Joshua Ulrich.
Ardia, D., Boudt, K., Carl, P., Mullen, K.M., Peterson, B.G. (2011) Differential Evolution with DEoptim. An Application to NonConvex Portfolio Optimization. R Journal, 3(1), 2734. doi: 10.32614/RJ2011005
Ardia, D., Ospina Arango, J.D., Giraldo Gomez, N.D. (2011) JumpDiffusion Calibration using Differential Evolution. Wilmott Magazine, 55 (September), 7679. doi: 10.1002/wilm.10034
Mullen, K.M, Ardia, D., Gil, D., Windover, D., Cline,J. (2011). DEoptim: An R Package for Global Optimization by Differential Evolution. Journal of Statistical Software, 40(6), 126. doi: 10.18637/jss.v040.i06
Price, K.V., Storn, R.M., Lampinen J.A. (2006) Differential Evolution  A Practical Approach to Global Optimization. Berlin Heidelberg: SpringerVerlag. ISBN 3540209506.
Zhang, J. and Sanderson, A. (2009) Adaptive Differential Evolution SpringerVerlag. ISBN 9783642015267
DEoptim
and DEoptimmethods
.
## set the population size to 20 DEoptim.control(NP = 20) ## set the population size, the number of iterations and don't ## display the iterations during optimization DEoptim.control(NP = 20, itermax = 100, trace = FALSE)
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