Title:  Global Optimization by Differential Evolution in C++ 

Description:  An efficient C++ based implementation of the 'DEoptim' function which performs global optimization by differential evolution. Its creation was motivated by trying to see if the old approximation "easier, shorter, faster: pick any two" could in fact be extended to achieving all three goals while moving the code from plain old C to modern C++. The initial version did in fact do so, but a good part of the gain was due to an implicit code review which eliminated a few inefficiencies which have since been eliminated in 'DEoptim'. 
Authors:  Dirk Eddelbuettel extending DEoptim (by David Ardia, Katharine Mullen, Brian Peterson, Joshua Ulrich) which itself is based on DEEngine (by Rainer Storn) 
Maintainer:  Dirk Eddelbuettel <[email protected]> 
License:  GPL (>= 2) 
Version:  0.1.7.1 
Built:  20240704 04:47:36 UTC 
Source:  https://github.com/eddelbuettel/rcppde 
Performs evolutionary global optimization via the Differential Evolution algorithm.
DEoptim(fn, lower, upper, control = DEoptim.control(), ...)
DEoptim(fn, lower, upper, control = DEoptim.control(), ...)
fn 
the function to be optimized (minimized). The function should have as its first
argument the vector of realvalued parameters to optimize, and return a scalar real result. 
lower , upper

two vectors specifying scalar real lower and upper bounds on each parameter to be optimized, so that the ith element
of 
control 
a list of control parameters; see 
... 
further arguments to be passed to 
DEoptim
performs optimization (minimization) of fn
.
The control
argument is a list; see the help file for
DEoptim.control
for details.
The R implementation of Differential Evolution (DE), DEoptim, was first published on the Comprehensive R Archive Network (CRAN) in 2005 by David Ardia. Early versions were written in pure R. Since version 2.00 (published to CRAN in 2009) the package has relied on an interface to a C implementation of DE, which is significantly faster on most problems as compared to the implementation in pure R. The C interface is in many respects similar to the MS Visual C++ v5.0 implementation of the Differential Evolution algorithm distributed with the book Differential Evolution – A Practical Approach to Global Optimization by Price, K.V., Storn, R.M., Lampinen J.A, SpringerVerlag, 2006. Since version 2.03 the C implementation dynamically allocates the memory required to store the population, removing limitations on the number of members in the population and length of the parameter vectors that may be optimized. Since becoming publicly available, the package DEoptim has been used by several authors to solve optimization problems arising in diverse domains; see Mullen et al. (2009) for a review.
To perform a maximization (instead of minimization) of a given
function, simply define a new function which is the opposite of the
function to maximize and apply DEoptim
to it.
To integrate additional constraints (than box constraints) on the parameters x
of
fn(x)
, for instance x[1] + x[2]^2 < 2
, integrate the
constraint within the function to optimize, for instance:
fn < function(x){ if (x[1] + x[2]^2 < 2){ r < Inf else{ ... } return(r) }
This simplistic strategy usually does not work all that well for gradientbased or Newtontype methods. It is likely to be alright when the solution is in the interior of the feasible region, but when the solution is on the boundary, optimization algorithm would have a difficult time converging. Furthermore, when the solution is on the boundary, this strategy would make the algorithm converge to an inferior solution in the interior. However, for methods such as DE which are not gradient based, this strategy might not be that bad.
Note that DEoptim
stops if any NA
or NaN
value is
obtained. You have to redefine your function to handle these values
(for instance, set NA
to Inf
in your objective function).
It is important to emphasize that the result of DEoptim
is a random variable,
i.e., different results will obtain when the algorithm is run repeatedly with the same
settings. Hence, the user should set the random seed if they want to reproduce the results, e.g., by
setting set.seed(1234)
before the call of DEoptim
.
DEoptim
relies on repeated evaluation of the objective function
in order to move the population toward a global minimum. Users
interested in making DEoptim
run as fast as possible should
ensure that evaluation of the objective function is as efficient as
possible. Using pure R code, this may often be accomplished
using vectorization. Writing parts of the objective function in a
lowerlevel language like C or Fortran may also increase speed.
Further details and examples of the R package DEoptim can be found in Mullen et al. (2009) and Ardia et al. (2010).
Please cite the package in publications.
The output of the function DEoptim
is a member of the S3
class DEoptim
. More precisely,
this is a list (of length 2) containing the following elements:
optim
, a list containing the following elements:
bestmem
: the best set of parameters found.
bestval
: the value of fn
corresponding to bestmem
.
nfeval
: number of function evaluations.
iter
: number of procedure iterations.
member
, a list containing the following elements:
lower
: the lower boundary.
upper
: the upper boundary.
bestvalit
: the best value of fn
at each iteration.
bestmemit
: the best member at each iteration.
pop
: the population generated at the last iteration.
storepop
: a list containing the intermediate populations.
Members of the class DEoptim
have a plot
method that
accepts the argument plot.type
. plot.type = "bestmemit"
results
in a plot of the parameter values that represent the lowest value of the objective function
each generation. plot.type = "bestvalit"
plots the best value of
the objective function each generation. Finally, plot.type = "storepop"
results in a plot of
stored populations (which are only available if these have been saved by
setting the control
argument of DEoptim
appropriately). Storing intermediate populations
allows us to examine the progress of the optimization in detail.
A summary method also exists and returns the best parameter vector, the best value of the objective function,
the number of generations optimization ran, and the number of times the
objective function was evaluated.
Differential Evolution (DE) is a search heuristic introduced by Storn and Price (1997). Its remarkable performance as a global optimization algorithm on continuous numerical minimization problems has been extensively explored; see Price et al. (2006). DE belongs to the class of genetic algorithms which use biologyinspired operations of crossover, mutation, and selection on a population in order to minimize an objective function over the course of successive generations (see Mitchell, 1998). As with other evolutionary algorithms, DE solves optimization problems by evolving a population of candidate solutions using alteration and selection operators. DE uses floatingpoint instead of bitstring encoding of population members, and arithmetic operations instead of logical operations in mutation. DE is particularly wellsuited to find the global optimum of a realvalued function of realvalued parameters, and does not require that the function be either continuous or differentiable.
Let $\mathit{NP}$
denote the number of parameter vectors (members) $x \in R^d$
in the population.
In order to create the initial generation, $\mathit{NP}$
guesses for the optimal value
of the parameter vector are made, either using random values between lower and upper
bounds (defined by the user) or using values given by
the user. Each generation involves creation of a new population from
the current population members $\{ x_i \,\, i = 1, \ldots, \mathit{NP}\}$
,
where $i$
indexes the vectors that make up the population.
This is accomplished using differential mutation of the
population members. An initial mutant parameter vector $v_i$
is
created by choosing three members of the population, $x_{r_0}$
,
$x_{r_1}$
and $x_{r_2}$
, at random. Then $v_i$
is
generated as
$v_i \doteq x_{r_0} + \mathit{F} \cdot (x_{r_1}  x_{r_2})$
where $\mathit{F}$
is a positive scale factor, effective values for which are
typically less than one. After the first mutation operation, mutation is
continued until $d$
mutations have been made, with a crossover probability
$\mathit{CR} \in [0,1]$
.
The crossover probability $\mathit{CR}$
controls the fraction of the parameter
values that are copied from the mutant. If an element of the trial parameter vector is found to violate the
bounds after mutation and crossover, it is reset in such a way that the bounds are respected (with the
specific protocol depending on the implementation).
Then, the objective function values associated with the children are determined. If a trial
vector has equal or lower objective function value than the previous
vector it replaces the previous vector in the population;
otherwise the previous vector remains. Variations of this scheme have also
been proposed; see Price et al. (2006) and DEoptim.control
.
Intuitively, the effect of the scheme is that the shape of the distribution of the population in the search space is converging with respect to size and direction towards areas with high fitness. The closer the population gets to the global optimum, the more the distribution will shrink and therefore reinforce the generation of smaller difference vectors.
As a general advice regarding the choice of $\mathit{NP}$
, $\mathit{F}$
and $\mathit{CR}$
,
Storn et al. (2006) state the following: Set the number
of parents $\mathit{NP}$
to 10 times the number of parameters, select weighting factor
$\mathit{F} = 0.8$
and crossover constant $\mathit{CR} = 0.9$
. Make sure that you initialize your parameter vectors
by exploiting their full numerical range, i.e., if a parameter is allowed to exhibit
values in the range [100, 100] it is a good idea to pick the initial values from this
range instead of unnecessarily restricting diversity. If you experience misconvergence in
the optimization process you usually have to increase the value for $\mathit{NP}$
, but often you only have to adjust
$\mathit{F}$
to be a little lower or higher than 0.8. If you increase
$\mathit{NP}$
and simultaneously lower $\mathit{F}$
a little, convergence is more
likely to occur but generally takes longer, i.e., DE is getting
more robust (there is always a convergence speed/robustness tradeoff).
DE is much more sensitive to the choice of $\mathit{F}$
than it is to
the choice of $\mathit{CR}$
. $\mathit{CR}$
is more like a fine tuning element. High
values of $\mathit{CR}$
like $\mathit{CR} = 1$
give faster convergence if convergence
occurs. Sometimes, however, you have to go down as much as $\mathit{CR} = 0$
to
make DE robust enough for a particular problem. For more details on the DE strategy, we refer
the reader to Storn and Price (1997) and Price et al. (2006).
For RcppDE: Dirk Eddelbuettel.
For DEoptim: David Ardia, Katharine Mullen [email protected], Brian Peterson and Joshua Ulrich.
Storn, R. and Price, K. (1997) Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces, Journal of Global Optimization, 11:4, 341–359.
Price, K.V., Storn, R.M., Lampinen J.A. (2006) Differential Evolution  A Practical Approach to Global Optimization. Berlin Heidelberg: SpringerVerlag. ISBN 3540209506.
Mitchell, M. (1998) An Introduction to Genetic Algorithms. The MIT Press. ISBN 0262631857.
Mullen, K.M., Ardia, D., Gil, D.L, Windover, D., Cline, J. (2009) DEoptim: An R Package for Global Optimization by Differential Evolution. URL https://www.ssrn.com/abstract=1526466
Ardia, D., Boudt, K., Carl, P., Mullen, K.M., Peterson, B.G. (2010) Differential Evolution (DEoptim) for NonConvex Portfolio Optimization. URL https://www.ssrn.com/abstract=1584905
DEoptim.control
for control arguments,
DEoptimmethods
for methods on DEoptim
objects,
including some examples in plotting the results;
optim
or constrOptim
for alternative optimization algorithms.
## Rosenbrock Banana function ## The function has a global minimum f(x) = 0 at the point (0,0). ## Note that the vector of parameters to be optimized must be the first ## argument of the objective function passed to DEoptim. Rosenbrock < function(x){ x1 < x[1] x2 < x[2] 100 * (x2  x1 * x1)^2 + (1  x1)^2 } ## DEoptim searches for minima of the objective function between ## lower and upper bounds on each parameter to be optimized. Therefore ## in the call to DEoptim we specify vectors that comprise the ## lower and upper bounds; these vectors are the same length as the ## parameter vector. lower < c(10,10) upper < lower ## run DEoptim and set a seed first for replicability set.seed(1234) DEoptim(Rosenbrock, lower, upper) ## increase the population size DEoptim(Rosenbrock, lower, upper, DEoptim.control(NP = 100)) ## change other settings and store the output outDEoptim < DEoptim(Rosenbrock, lower, upper, DEoptim.control(NP = 80, itermax = 400, F = 1.2, CR = 0.7)) ## plot the output plot(outDEoptim) ## 'Wild' function, global minimum at about 15.81515 Wild < function(x) 10 * sin(0.3 * x) * sin(1.3 * x^2) + 0.00001 * x^4 + 0.2 * x + 80 plot(Wild, 50, 50, n = 1000, main = "'Wild function'") outDEoptim < DEoptim(Wild, lower = 50, upper = 50, control = DEoptim.control(trace = FALSE)) plot(outDEoptim) DEoptim(Wild, lower = 50, upper = 50, control = DEoptim.control(NP = 50))
## Rosenbrock Banana function ## The function has a global minimum f(x) = 0 at the point (0,0). ## Note that the vector of parameters to be optimized must be the first ## argument of the objective function passed to DEoptim. Rosenbrock < function(x){ x1 < x[1] x2 < x[2] 100 * (x2  x1 * x1)^2 + (1  x1)^2 } ## DEoptim searches for minima of the objective function between ## lower and upper bounds on each parameter to be optimized. Therefore ## in the call to DEoptim we specify vectors that comprise the ## lower and upper bounds; these vectors are the same length as the ## parameter vector. lower < c(10,10) upper < lower ## run DEoptim and set a seed first for replicability set.seed(1234) DEoptim(Rosenbrock, lower, upper) ## increase the population size DEoptim(Rosenbrock, lower, upper, DEoptim.control(NP = 100)) ## change other settings and store the output outDEoptim < DEoptim(Rosenbrock, lower, upper, DEoptim.control(NP = 80, itermax = 400, F = 1.2, CR = 0.7)) ## plot the output plot(outDEoptim) ## 'Wild' function, global minimum at about 15.81515 Wild < function(x) 10 * sin(0.3 * x) * sin(1.3 * x^2) + 0.00001 * x^4 + 0.2 * x + 80 plot(Wild, 50, 50, n = 1000, main = "'Wild function'") outDEoptim < DEoptim(Wild, lower = 50, upper = 50, control = DEoptim.control(trace = FALSE)) plot(outDEoptim) DEoptim(Wild, lower = 50, upper = 50, control = DEoptim.control(NP = 50))
Methods for DEoptim objects.
## S3 method for class 'DEoptim' summary(object, ...) ## S3 method for class 'DEoptim' plot(x, plot.type = c("bestmemit", "bestvalit", "storepop"), ...)
## S3 method for class 'DEoptim' summary(object, ...) ## S3 method for class 'DEoptim' plot(x, plot.type = c("bestmemit", "bestvalit", "storepop"), ...)
object 
an object of class 
x 
an object of class 
plot.type 
should we plot the best member at each iteration, the best value at each iteration or the intermediate populations? 
... 
further arguments passed to or from other methods. 
Members of the class DEoptim
have a plot
method that
accepts the argument plot.type
. plot.type = "bestmemit"
results
in a plot of the parameter values that represent the lowest value of the objective function
each generation. plot.type = "bestvalit"
plots the best value of
the objective function each generation. Finally, plot.type = "storepop"
results in a plot of
stored populations (which are only available if these have been saved by
setting the control
argument of DEoptim
appropriately). Storing intermediate populations
allows us to examine the progress of the optimization in detail.
A summary method also exists and returns the best parameter vector, the best value of the objective function,
the number of generations optimization ran, and the number of times the
objective function was evaluated.
Further details and examples of the R package DEoptim can be found in Mullen et al. (2009) and Ardia et al. (2010).
Please cite the package in publications.
For RcppDE: Dirk Eddelbuettel.
For DEoptim: David Ardia, Katharine Mullen [email protected], Brian Peterson and Joshua Ulrich.
Mullen, K.M., Ardia, D., Gil, D.L, Windover, D., Cline, J. (2009) DEoptim: An R Package for Global Optimization by Differential Evolution. URL https://www.ssrn.com/abstract=1526466
Ardia, D., Boudt, K., Carl, P., Mullen, K.M., Peterson, B.G. (2010) Differential Evolution (DEoptim) for NonConvex Portfolio Optimization. URL https://www.ssrn.com/abstract=1584905
DEoptim
and DEoptim.control
.
## Rosenbrock Banana function ## The function has a global minimum f(x) = 0 at the point (0,0). ## Note that the vector of parameters to be optimized must be the first ## argument of the objective function passed to DEoptim. Rosenbrock < function(x){ x1 < x[1] x2 < x[2] 100 * (x2  x1 * x1)^2 + (1  x1)^2 } lower < c(10, 10) upper < lower set.seed(1234) outDEoptim < DEoptim(Rosenbrock, lower, upper) ## print output information summary(outDEoptim) ## plot the best members plot(outDEoptim, type = 'b') ## plot the best values dev.new() plot(outDEoptim, plot.type = "bestvalit", type = 'b', col = 'blue') ## rerun the optimization, and store intermediate populations outDEoptim < DEoptim(Rosenbrock, lower, upper, DEoptim.control(itermax = 500, storepopfrom = 1, storepopfreq = 2)) summary(outDEoptim) ## plot intermediate populations dev.new() plot(outDEoptim, plot.type = "storepop") ## Wild function Wild < function(x) 10 * sin(0.3 * x) * sin(1.3 * x^2) + 0.00001 * x^4 + 0.2 * x + 80 outDEoptim = DEoptim(Wild, lower = 50, upper = 50, DEoptim.control(trace = FALSE, storepopfrom = 50, storepopfreq = 1)) plot(outDEoptim, type = 'b') dev.new() plot(outDEoptim, plot.type = "bestvalit", type = 'b') ## Not run: ## an example with a normal mixture model: requires package mvtnorm library(mvtnorm) ## neg value of the density function negPdfMix < function(x) { tmp < 0.5 * dmvnorm(x, c(3, 3)) + 0.5 * dmvnorm(x, c(3, 3)) tmp } ## wrapper plotting function plotNegPdfMix < function(x1, x2) negPdfMix(cbind(x1, x2)) ## contour plot of the mixture x1 < x2 < seq(from = 10.0, to = 10.0, by = 0.1) thexlim < theylim < range(x1) z < outer(x1, x2, FUN = plotNegPdfMix) contour(x1, x2, z, nlevel = 20, las = 1, col = rainbow(20), xlim = thexlim, ylim = theylim) set.seed(1234) outDEoptim < DEoptim(negPdfMix, c(10, 10), c(10, 10), DEoptim.control(NP = 100, itermax = 100, storepopfrom = 1, storepopfreq = 5)) ## convergence plot dev.new() plot(outDEoptim) ## the intermediate populations indicate the bimodality of the function dev.new() plot(outDEoptim, plot.type = "storepop") ## End(Not run)
## Rosenbrock Banana function ## The function has a global minimum f(x) = 0 at the point (0,0). ## Note that the vector of parameters to be optimized must be the first ## argument of the objective function passed to DEoptim. Rosenbrock < function(x){ x1 < x[1] x2 < x[2] 100 * (x2  x1 * x1)^2 + (1  x1)^2 } lower < c(10, 10) upper < lower set.seed(1234) outDEoptim < DEoptim(Rosenbrock, lower, upper) ## print output information summary(outDEoptim) ## plot the best members plot(outDEoptim, type = 'b') ## plot the best values dev.new() plot(outDEoptim, plot.type = "bestvalit", type = 'b', col = 'blue') ## rerun the optimization, and store intermediate populations outDEoptim < DEoptim(Rosenbrock, lower, upper, DEoptim.control(itermax = 500, storepopfrom = 1, storepopfreq = 2)) summary(outDEoptim) ## plot intermediate populations dev.new() plot(outDEoptim, plot.type = "storepop") ## Wild function Wild < function(x) 10 * sin(0.3 * x) * sin(1.3 * x^2) + 0.00001 * x^4 + 0.2 * x + 80 outDEoptim = DEoptim(Wild, lower = 50, upper = 50, DEoptim.control(trace = FALSE, storepopfrom = 50, storepopfreq = 1)) plot(outDEoptim, type = 'b') dev.new() plot(outDEoptim, plot.type = "bestvalit", type = 'b') ## Not run: ## an example with a normal mixture model: requires package mvtnorm library(mvtnorm) ## neg value of the density function negPdfMix < function(x) { tmp < 0.5 * dmvnorm(x, c(3, 3)) + 0.5 * dmvnorm(x, c(3, 3)) tmp } ## wrapper plotting function plotNegPdfMix < function(x1, x2) negPdfMix(cbind(x1, x2)) ## contour plot of the mixture x1 < x2 < seq(from = 10.0, to = 10.0, by = 0.1) thexlim < theylim < range(x1) z < outer(x1, x2, FUN = plotNegPdfMix) contour(x1, x2, z, nlevel = 20, las = 1, col = rainbow(20), xlim = thexlim, ylim = theylim) set.seed(1234) outDEoptim < DEoptim(negPdfMix, c(10, 10), c(10, 10), DEoptim.control(NP = 100, itermax = 100, storepopfrom = 1, storepopfreq = 5)) ## convergence plot dev.new() plot(outDEoptim) ## the intermediate populations indicate the bimodality of the function dev.new() plot(outDEoptim, plot.type = "storepop") ## End(Not run)
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 = 50, itermax = 200, CR = 0.5, F = 0.8, trace = TRUE, initialpop = NULL, storepopfrom = itermax + 1, storepopfreq = 1, p = 0.2, c = 0, reltol = sqrt(.Machine$double.eps), steptol = itermax)
DEoptim.control(VTR = Inf, strategy = 2, bs = FALSE, NP = 50, itermax = 200, CR = 0.5, F = 0.8, trace = TRUE, initialpop = NULL, storepopfrom = itermax + 1, storepopfreq = 1, p = 0.2, c = 0, reltol = sqrt(.Machine$double.eps), steptol = itermax)
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 
stepsize from interval [0,2]. Default to 
trace 
Printing of progress occurs? Default to 
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 
when 
reltol 
relative convergence tolerance. The algorithm stops if
it is unable to reduce the value by a factor of 
steptol 
see 
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
$v_{i,g} = old_{i,g} + (best_{g}  old_{i,g}) + x_{r0,g} + F \cdot (x_{r1,g}  x_{r2,g})$
is used, where $old_{i,g}$
and $best_{g}$
are the
$i$
th 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
$v_{i,g} = best_{g} + jitter + F \cdot (x_{r1,g}  x_{r2,g})$
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
$v_{i,g} = x_{r0,g} + dither \cdot (x_{r1,g}  x_{r2,g})$
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.
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
$v_{i,g} = x_{r0,g} + 0.5 \cdot (F + 1) \cdot (x_{r1,g} + x_{r2,g}  2 \cdot x_{r0,g})$
is used.
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. (2009) and Ardia et al. (2010).
Please cite the package in publications.
For RcppDE: Dirk Eddelbuettel.
For DEoptim: David Ardia, Katharine Mullen [email protected], Brian Peterson and Joshua Ulrich.
Price, K.V., Storn, R.M., Lampinen J.A. (2006) Differential Evolution  A Practical Approach to Global Optimization. Berlin Heidelberg: SpringerVerlag. ISBN 3540209506.
Mullen, K.M., Ardia, D., Gil, D.L, Windover, D., Cline, J. (2009) DEoptim: An R Package for Global Optimization by Differential Evolution. URL https://www.ssrn.com/abstract=1526466
Ardia, D., Boudt, K., Carl, P., Mullen, K.M., Peterson, B.G. (2010) Differential Evolution (DEoptim) for NonConvex Portfolio Optimization. URL https://www.ssrn.com/abstract=1584905
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)
## 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)