pal.math
Class DifferentialEvolution

java.lang.Object
  pal.math.MultivariateMinimum
      pal.math.DifferentialEvolution

public class DifferentialEvolution
extends MultivariateMinimum

global minimization of a real-valued function of several variables without using derivatives using a genetic algorithm (Differential Evolution)

Author:

Korbinian Strimmer

Nested Class Summary

Nested classes/interfaces inherited from class pal.math.MultivariateMinimum

MultivariateMinimum.Factory

Field Summary

double	CR Crossing over factor (default 0.9)
double	F weight factor (default 0.7)
int	prin variable controlling print out, default value = 0 (0 -> no output, 1 -> print final value, 2 -> detailed map of optimization process)

Fields inherited from class pal.math.MultivariateMinimum

maxFun, numFun, numFuncStops

Constructor Summary

DifferentialEvolution(int dim)
construct DE optimization modul (population size is selected automatically)

DifferentialEvolution(int dim, int popSize)
construct optimization modul

Method Summary

void	optimize(MultivariateFunction func, double[] xvec, double tolfx, double tolx) The actual optimization routine (needs to be implemented in a subclass of MultivariateMinimum).
void	optimize(MultivariateFunction func, double[] xvec, double tolfx, double tolx, MinimiserMonitor monitor) The actual optimization routine It finds a minimum close to vector x when the absolute tolerance for each parameter is specified.

Methods inherited from class pal.math.MultivariateMinimum

copy, findMinimum, findMinimum, findMinimum, stopCondition

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

F

public double F

weight factor (default 0.7)

CR

public double CR

Crossing over factor (default 0.9)

prin

public int prin

variable controlling print out, default value = 0 (0 -> no output, 1 -> print final value, 2 -> detailed map of optimization process)

Constructor Detail

DifferentialEvolution

public DifferentialEvolution(int dim)

construct DE optimization modul (population size is selected automatically)

DE web page: http://www.icsi.berkeley.edu/~storn/code.html

Parameters:

dim - dimension of optimization vector

DifferentialEvolution

public DifferentialEvolution(int dim,
                             int popSize)

construct optimization modul

Parameters:

dim - dimension of optimization vector

popSize - population size

Method Detail

optimize

public void optimize(MultivariateFunction func,
                     double[] xvec,
                     double tolfx,
                     double tolx)

Description copied from class: MultivariateMinimum

The actual optimization routine (needs to be implemented in a subclass of MultivariateMinimum). It finds a minimum close to vector x when the absolute tolerance for each parameter is specified.

Specified by:

optimize in class MultivariateMinimum

Parameters:

func - multivariate function

xvec - initial guesses for the minimum (contains the location of the minimum on return)

tolfx - absolute tolerance of function value

tolx - absolute tolerance of each parameter

optimize

public void optimize(MultivariateFunction func,
                     double[] xvec,
                     double tolfx,
                     double tolx,
                     MinimiserMonitor monitor)

Description copied from class: MultivariateMinimum

The actual optimization routine It finds a minimum close to vector x when the absolute tolerance for each parameter is specified.

Overrides:

optimize in class MultivariateMinimum

Parameters:

func - multivariate function

xvec - initial guesses for the minimum (contains the location of the minimum on return)

tolfx - absolute tolerance of function value

tolx - absolute tolerance of each parameter

monitor - A monitor object that receives information about the minimising process (for display purposes)