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 = TRUE, 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 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.

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.

See Also

DEoptim and DEoptim-methods.

Examples

DEoptim.control(NP = 20)

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

[Package DEoptim version 2.0-1 Index]