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eodev/eo/src/es/eoEsChromInit.h
kuepper d418459a01 Install eoPropGAGenOp.h
Add #include <cmath> in eoEsChromInit.h in order to make gcc-4.0 happy.
2005-08-29 07:32:13 +00:00

152 lines
4.7 KiB
C++

// -*- mode: c++; c-indent-level: 4; c++-member-init-indent: 8; comment-column: 35; -*-
//-----------------------------------------------------------------------------
// eoEsChromInit.h
// (c) Maarten Keijzer 2000, GeNeura Team, 1998 - EEAAX 1999
/*
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 of the License, or (at your option) any later version.
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.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
Contact: todos@geneura.ugr.es, http://geneura.ugr.es
Marc.Schoenauer@polytechnique.fr
mak@dhi.dk
*/
//-----------------------------------------------------------------------------
#ifndef _eoEsChromInit_H
#define _eoEsChromInit_H
#include <cmath>
#include <es/eoRealInitBounded.h>
#include <es/eoEsSimple.h>
#include <es/eoEsStdev.h>
#include <es/eoEsFull.h>
#ifndef M_PI
#define M_PI 3.1415926535897932384626433832795
#endif
/**
\ingroup EvolutionStrategies
Random Es-chromosome initializer (therefore derived from eoInit)
This class can initialize four types of real-valued genotypes
thanks to tempate specialization of private method create
eoReal just an eoVector<double>
eoEsSimple + one self-adapting single sigma for all variables
eoEsStdev a whole std::vector of self-adapting sigmas
eoEsFull a full self-adapting correlation matrix
@see eoReal eoEsSimple eoEsStdev eoEsFull eoInit
*/
template <class EOT>
class eoEsChromInit : public eoRealInitBounded<EOT>
{
public:
using eoEsChromInit< EOT >::size;
using eoEsChromInit< EOT >::theBounds;
typedef typename EOT::Fitness FitT;
/** Ctor:
@param eoRealVectorBounds& _bounds : bounds for uniform initialization
@param _sigma : initial value for the stddev
@param _to_scale : wether sigma should be multiplied by the range of each variable
added December 2004 - MS (together with the whole comment :-)
*/
eoEsChromInit(eoRealVectorBounds& _bounds, double _sigma = 0.3, bool _to_scale=false)
: eoRealInitBounded<EOT>(_bounds)
{
// a bit of pre-computations, to save time later (even if some are useless)
// first, the case of one unique sigma
if (_to_scale) // sigma is scaled by the average range (if that means anything!)
{
double scaleUnique = 0;
for (unsigned i=0; i<size(); i++)
scaleUnique += theBounds().range(i);
scaleUnique /= size();
uniqueSigma = _sigma * scaleUnique;
}
else
uniqueSigma = _sigma;
// now the case of a vector of sigmas
// first allocate
lesSigmas.resize(size()); // size() is the size of the bounds (see eoRealInitBounded)
for (unsigned i=0; i<size(); i++)
if (_to_scale) // each sigma is scaled by the range of the corresponding variable
{
lesSigmas[i] = _sigma * theBounds().range(i);
}
else
lesSigmas[i] = _sigma;
}
void operator()(EOT& _eo)
{
eoRealInitBounded<EOT>::operator()(_eo);
create_self_adapt(_eo);
_eo.invalidate(); // was MISSING!!!!
}
// accessor to sigma
// double sigmaInit() {return sigma;}
private :
// No adaptive mutation at all
void create_self_adapt(eoReal<FitT>&)// nothing to do here ...
{ }
// Adaptive mutation through a unique sigma
void create_self_adapt(eoEsSimple<FitT>& result)
{
// pre-computed in the Ctor
result.stdev = uniqueSigma;
}
// Adaptive mutation through a std::vector of sigmas
void create_self_adapt(eoEsStdev<FitT>& result)
{
result.stdevs = lesSigmas;
}
// Adaptive mutation through a whole correlation matrix
void create_self_adapt(eoEsFull<FitT>& result)
{
// first the stdevs (pre-computed in the Ctor)
result.stdevs = lesSigmas;
unsigned int theSize = size();
// nb of rotation angles: N*(N-1)/2 (in general!)
result.correlations.resize(theSize*(theSize - 1) / 2);
for (unsigned i=0; i<result.correlations.size(); ++i)
{
// uniform in [-PI, PI)
result.correlations[i] = rng.uniform(2 * M_PI) - M_PI;
}
}
// the DATA
double uniqueSigma; // initial value in case of a unique sigma
std::vector<double> lesSigmas; // initial values in case of a vector fo sigmas
};
#endif