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Added CMA

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maartenkeijzer 2005-10-14 15:33:32 +00:00
commit 8b03a5232a
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eo/src/es/CMAParams.cpp Normal file
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/*
* C++ification of Nikolaus Hansen's original C-source code for the
* CMA-ES
*
* C++-ificiation performed by Maarten Keijzer (C) 2005. Licensed under
* the LGPL. Original copyright of Nikolaus Hansen can be found below
*
*
*
*/
/* --------------------------------------------------------- */
/* --------------------------------------------------------- */
/* --- File: cmaes.c -------- Author: Nikolaus Hansen --- */
/* --------------------------------------------------------- */
/*
* CMA-ES for non-linear function minimization.
*
* Copyright (C) 1996, 2003 Nikolaus Hansen.
* e-mail: hansen@bionik.tu-berlin.de
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later
* version (see http://www.gnu.org/copyleft/lesser.html).
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* */
/* --- Changes : ---
* 03/03/21: argument const double *rgFunVal of
* cmaes_ReestimateDistribution() was treated incorrectly.
* 03/03/29: restart via cmaes_resume_distribution() implemented.
* 03/03/30: Always max std dev / largest axis is printed first.
* 03/08/30: Damping is adjusted for large mueff.
* 03/10/30: Damping is adjusted for large mueff always.
* 04/04/22: Cumulation time and damping for step size adjusted.
* No iniphase but conditional update of pc.
* Version 2.23.
* */
#include <es/CMAParams.h>
#include <utils/eoParser.h>
#include <string>
using namespace std;
namespace eo {
CMAParams::CMAParams(eoParser& parser) {
string section = "CMA parameters";
n = parser.createParam(0, "dimensionality", "Dimensionality (N) of the problem", 'N', section, true).value();
maxgen = parser.createParam(
1000,
"max-gen",
"Maximum number of generations that the system will run (needed for damping)",
'M',
section).value();
if (n == 0) {
return;
}
defaults(n, maxgen);
/* handle lambda */
lambda = parser.createParam(
lambda,
"lambda",
"Number of offspring",
'l',
section).value();
if (lambda < 2) {
lambda = 4+(int)(3*log((double) n));
cerr << "Too small lambda specified, setting it to " << lambda << endl;
}
/* handle mu */
mu = parser.createParam(
mu,
"mu",
"Population size",
'm',
section).value();
if (mu >= lambda) {
mu = lambda/2;
cerr << "Mu set larger/equal to lambda, setting it to " << mu << endl;
}
/* handle selection weights */
int weight_type = parser.createParam(
0,
"weighting",
"Weighting scheme (for 'selection'): 0 = logarithmic, 1 = equal, 2 = linear",
'w',
section).value();
switch (weight_type) {
case 1:
{
for (unsigned i = 0; i < weights.size(); ++i) {
weights[i] = mu - i;
}
}
case 2:
{
weights = 1.;
}
default :
{
for (unsigned i = 0; i < weights.size(); ++i) {
weights[i] = log(mu+1.)-log(i+1.);
}
}
}
/* Normalize weights and set mu_eff */
double sumw = weights.sum();
mueff = sumw * sumw / (weights * weights).sum();
weights /= sumw;
/* most of the rest depends on mu_eff, so needs to be set again */
/* set the others using Nikolaus logic. If you want to tweak, you can parameterize over these defaults */
mucov = mueff;
ccumsig = (mueff + 2.) / (n + mueff + 3.);
ccumcov = 4. / (n + 4);
double t1 = 2. / ((n+1.4142)*(n+1.4142));
double t2 = (2.*mucov-1.) / ((n+2.)*(n+2.)+mucov);
t2 = (t2 > 1) ? 1 : t2;
t2 = (1./mucov) * t1 + (1.-1./mucov) * t2;
ccov = t2;
damp = 1 + std::max(0.3,(1.-(double)n/(double)maxgen))
* (1+2*std::max(0.,sqrt((mueff-1.)/(n+1.))-1)) /* limit sigma increase */
/ ccumsig;
vector<double> mins(1,0.0);
mins = parser.createParam(
mins,
"min-stdev",
"Array of minimum stdevs, last one will apply for all remaining axes",
0,
section).value();
if (mins.size() > n) mins.resize(n);
if (mins.size()) {
minStdevs = mins.back();
for (unsigned i = 0; i < mins.size(); ++i) {
minStdevs[i] = mins[i];
}
}
vector<double> inits(1,0.3);
inits = parser.createParam(
inits,
"init-stdev",
"Array of initial stdevs, last one will apply for all remaining axes",
0,
section).value();
if (inits.size() > n) inits.resize(n);
if (inits.size()) {
initialStdevs = inits.back();
for (unsigned i = 0; i < inits.size(); ++i) {
initialStdevs[i] = inits[i];
}
}
}
void CMAParams::defaults(unsigned n_, unsigned maxgen_) {
n = n_;
maxgen = maxgen_;
lambda = 4+(int)(3*log((double) n));
mu = lambda / 2;
weights.resize(mu);
for (unsigned i = 0; i < weights.size(); ++i) {
weights[i] = log(mu+1.)-log(i+1.);
}
/* Normalize weights and set mu_eff */
double sumw = weights.sum();
mueff = sumw * sumw / (weights * weights).sum();
weights /= sumw;
mucov = mueff;
ccumsig *= (mueff + 2.) / (n + mueff + 3.);
ccumcov = 4. / (n + 4);
double t1 = 2. / ((n+1.4142)*(n+1.4142));
double t2 = (2.*mucov-1.) / ((n+2.)*(n+2.)+mucov);
t2 = (t2 > 1) ? 1 : t2;
t2 = (1./mucov) * t1 + (1.-1./mucov) * t2;
ccov = t2;
damp = 1 + std::max(0.3,(1.-(double)n/maxgen))
* (1+2*std::max(0.,sqrt((mueff-1.)/(n+1.))-1)) /* limit sigma increase */
/ ccumsig;
minStdevs.resize(n);
minStdevs = 0.0;
initialStdevs.resize(n);
initialStdevs = 0.3;
}
}// namespace eo