simpleEA: Simple Evolutionary Algorithm

In adagio: Discrete and Global Optimization Routines

Simple Evolutionary Algorithm

Description

Simple Evolutionary Algorithm for Minimization.

Usage

simpleEA(fn, lower, upper, N = 100, ..., con = 0.1, new = 0.05,
         tol = 1e-10, eps = 1e-07, scl = 1/2, confined = FALSE, log = FALSE)

Arguments

fn	the objective function to be minimized.
lower	vector of lower bounds for all coordinates.
upper	vector of upper bounds for all coordinates.
N	number of children per parent.
...	additional parameters to be passed to the function.
con	percentage of individuals concentrating to the best parents.
new	percentage of new individuals not focussed on existing parents.
tol	tolerance; if in the last three loops no better individuals were found up to this tolerance, stop.
eps	grid size bound to be reached.
scl	scaling factor for shrinking the grid.
confined	logical; shall the set of individuals be strictly respect the boundary? Default: FALSE.
log	logical, should best solution found be printed per step.

Details

Evolutionary search to minimize a function: For each point in the current generation, n random points are introduced and the n best results of each generation (and its parents) are used to form the next generation.

The scale shrinks the generation of new points as the algorithm proceeds. It is possible for some children to lie outside the given rectangle, and therefore the final result may lie outside the unit rectangle well. (TO DO: Make this an option.)

Value

List with the following components:

par	numeric vector representing the minimum found.
val	function value at the minimum found.
fun.calls	number of function calls made.
rel.scl	last scaling factor indicating grid size in last step.
rel.tol	relative tolerance within the last three minima found.

Note

Original Mathematica Version by Stan Wagon in the SIAM textbook. Translated to R by Hans W Borchers.

Author(s)

HwB <[e-mail]>

References

Stan Wagon. “Think Globally, Act Locally". Chapter 4 In: F. Bornemann, D. Laurie, S. Wagon, and J. Waldvogel (Eds.). The SIAM 100-Digit Challenge. Society of Industrial and Applied Mathematics, 2004.

See Also

DEoptim in the ‘DEoptim’ package.

Examples

  simpleEA(fnTrefethen, lower=c(-1,-1), upper=c(1,1), log=FALSE)
  # $par
  # [1] -0.02440310  0.21061243  # this is the true global optimum!
  # $val
  # [1] -3.306869

adagio documentation built on Oct. 26, 2023, 5:08 p.m.