adagio (version 0.5.9)

simpleEA: 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.

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.

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.)

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(trefethen, 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