DEoptim.control: Control various aspects of the DEoptim implementation
In RcppDE: Global optimization by differential evolution in C++

Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/DEoptim.R

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, checkWinner = FALSE, avWinner = TRUE, p = 0.2)

`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 (default) `3`: DE / best / 1 / bin with jitter `4`: DE / rand / 1 / bin with per-vector-dither `5`: DE / rand / 1 / bin with per-generation-dither `6`: DE / current-to-p-best / 1 any value not above: variation to DE / rand / 1 / bin: either-or-algorithm. Default strategy is currently `2`. See Details.
`bs`	if `FALSE` then every mutant will be tested against a member in the previous generation, and the best value will proceed into the next generation (this is standard trial vs. target selection). If `TRUE` then the old generation and `NP` mutants will be sorted by their associated objective function values, and the best `NP` vectors will proceed into the next generation (best of parent and child selection). Default is `FALSE`.
`NP`	number of population members. Defaults to `50`. For many problems it is best to set `NP` to be at least 10 times the length of the parameter vector.
`itermax`	the maximum iteration (population generation) allowed. Default is `200`.
`CR`	crossover probability from interval [0,1]. Default to `0.9`.
`F`	step-size from interval [0,2]. Default to `0.8`.
`trace`	Printing of progress occurs? Default to `TRUE`. If numeric, progress will be printed every `trace` iterations.
`initialpop`	an initial population used as a starting population in the optimization procedure. May be useful to speed up the convergence. Default to `NULL`. If given, each member of the initial population should be given as a row of a numeric matrix, so that `initialpop` is a matrix with `NP` rows and a number of columns equal to the length of the parameter vector to be optimized.
`storepopfrom`	from which generation should the following intermediate populations be stored in memory. Default to `itermax + 1`, i.e., no intermediate population is stored.
`storepopfreq`	the frequency with which populations are stored. Default to `1`, i.e., every intermediate population is stored.
`checkWinner`	logical value indicating whether to re-evaluate the objective function using the winning parameter vector if this vector remains the same between generations. This may be useful for the optimization of a noisy objective function. If `checkWinner = TRUE` and `avWinner = FALSE` then the value associated with re-evaluation of the objective function is used in the next generation. Default to `FALSE`.
`avWinner`	logical value. If `checkWinner = TRUE` and `avWinner = TRUE` then the objective function value associated with the winning member represents the average of all evaluations of the objective function over the course of the ‘winning streak’ of the best population member. This option may be useful for optimization of noisy objective functions, and is interpreted only if `checkWinner = TRUE`. The default value is `TRUE`.
`p`	when `strategy = 6`, the top (100 * p)% best solutions are used in the mutation. `p` must be defined in (0,1].

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 / local-to-best / 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 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 * (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 per-generation.
any value not above: variation to DE / rand / 1 / bin: either-or 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.

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 katharine.mullen@nist.gov, 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: Springer-Verlag. 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 http://ssrn.com/abstract=1526466

Ardia, D., Boudt, K., Carl, P., Mullen, K.M., Peterson, B.G. (2010) Differential Evolution (DEoptim) for Non-Convex Portfolio Optimization. URL http://ssrn.com/abstract=1584905

DEoptim and DEoptim-methods.

## 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)

Now loading:
  RcppDE: C++ Implementation of Differential Evolution Optimisation
  Author: Dirk Eddelbuettel
Based on:
  DEoptim (version 2.0-7): Differential Evolution algorithm in R
  Authors: David Ardia, Katharine Mullen, Brian Peterson and Joshua Ulrich
$VTR
[1] -Inf

$strategy
[1] 2

$NP
[1] 20

$itermax
[1] 200

$CR
[1] 0.5

$F
[1] 0.8

$bs
[1] FALSE

$trace
[1] TRUE

$initialpop
NULL

$storepopfrom
[1] 201

$storepopfreq
[1] 1

$p
[1] 0.2

$c
[1] 0

$reltol
[1] 1.490116e-08

$steptol
[1] 200

$VTR
[1] -Inf

$strategy
[1] 2

$NP
[1] 20

$itermax
[1] 100

$CR
[1] 0.5

$F
[1] 0.8

$bs
[1] FALSE

$trace
[1] FALSE

$initialpop
NULL

$storepopfrom
[1] 101

$storepopfreq
[1] 1

$p
[1] 0.2

$c
[1] 0

$reltol
[1] 1.490116e-08

$steptol
[1] 100