R: Control various aspects of the DEoptim implementation

DEoptim.control {DEoptim}

R Documentation

Control various aspects of the DEoptim implementation

Description

Allow the user to set some characteristics of the differential evolution optimization algorithm implemented in DEoptim.

Usage

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)

Arguments

`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 jitter `4`: DE / rand / 1 / bin with per-vector-dither `5`: DE / rand / 1 / bin with per-generation-dither any value not above: variation to DE / rand / 1 / bin: either-or-algorithm.
`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).
`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`	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. May be 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 stored.

Value

A list with components:

`VTR`
`strategy`
`bs`
`NP`
`itermax`
`CR`
`F`
`trace`

with meanings as explained under ‘Arguments’.

Note

Please cite the package in publications. Use citation("DEoptim").

Author(s)

David Ardia david.ardia@unifr.ch and Katharine Mullen katharine.mullen@nist.gov.

References

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.

Examples

DEoptim.control(NP = 20)

DEoptim.control(NP = 20, itermax = 100, trace = FALSE)

[Package DEoptim version 2.0-3 Index]