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biteopt.h
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biteopt.h
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//$ nocpp
/**
* @file biteopt.h
*
* @brief The inclusion file for the CBiteOpt and CBiteOptDeep classes.
*
* Description is available at https://github.com/avaneev/biteopt
*
* E-mail: aleksey.vaneev@gmail.com or info@voxengo.com
*
* @section license License
*
* Copyright (c) 2016-2024 Aleksey Vaneev
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
* DEALINGS IN THE SOFTWARE.
*/
#ifndef BITEOPT_INCLUDED
#define BITEOPT_INCLUDED
#define BITEOPT_VERSION "2024.6"
#include "spheropt.h"
#include "mbopt.h"
/**
* BiteOpt optimization class. Implements a stochastic non-linear
* bound-constrained derivative-free optimization method.
*
* Description is available at https://github.com/avaneev/biteopt
*/
class CBiteOpt : public CBiteOptBase< int64_t >
{
public:
typedef int64_t ptype; ///< Parameter value storage type (should be a
///< signed integer type, same as CBiteOptBase template parameter).
CBiteOpt()
: ParOpt( this )
, ParOpt2( this )
{
addSel( MethodSel, "MethodSel" );
addSel( M1Sel, "M1Sel" );
addSel( M1ASel, "M1ASel" );
addSel( M1BSel, "M1BSel" );
addSel( M1CSel, "M1CSel" );
addSel( M2Sel, "M2Sel" );
addSel( M2BSel, "M2BSel" );
addSel( PopChangeIncrSel, "PopChangeIncrSel" );
addSel( PopChangeDecrSel, "PopChangeDecrSel" );
addSel( ParOpt2Sel, "ParOpt2Sel" );
addSel( ParPopPSel[ 0 ], "ParPopPSel[ 0 ]" );
addSel( ParPopPSel[ 1 ], "ParPopPSel[ 1 ]" );
addSel( ParPopPSel[ 2 ], "ParPopPSel[ 2 ]" );
addSel( ParPopPSel[ 3 ], "ParPopPSel[ 3 ]" );
addSel( ParPopPSel[ 4 ], "ParPopPSel[ 4 ]" );
addSel( ParPopPSel[ 5 ], "ParPopPSel[ 5 ]" );
addSel( ParPopPSel[ 6 ], "ParPopPSel[ 6 ]" );
addSel( ParPopPSel[ 7 ], "ParPopPSel[ 7 ]" );
addSel( AltPopPSel, "AltPopPSel" );
addSel( AltPopSel[ 0 ], "AltPopSel[ 0 ]" );
addSel( AltPopSel[ 1 ], "AltPopSel[ 1 ]" );
addSel( AltPopSel[ 2 ], "AltPopSel[ 2 ]" );
addSel( AltPopSel[ 3 ], "AltPopSel[ 3 ]" );
addSel( OldPopSel, "OldPopSel" );
addSel( MinSolPwrSel[ 0 ], "MinSolPwrSel[ 0 ]" );
addSel( MinSolPwrSel[ 1 ], "MinSolPwrSel[ 1 ]" );
addSel( MinSolPwrSel[ 2 ], "MinSolPwrSel[ 2 ]" );
addSel( MinSolPwrSel[ 3 ], "MinSolPwrSel[ 3 ]" );
addSel( MinSolMulSel[ 0 ], "MinSolMulSel[ 0 ]" );
addSel( MinSolMulSel[ 1 ], "MinSolMulSel[ 1 ]" );
addSel( MinSolMulSel[ 2 ], "MinSolMulSel[ 2 ]" );
addSel( MinSolMulSel[ 3 ], "MinSolMulSel[ 3 ]" );
addSel( Gen1AllpSel, "Gen1AllpSel" );
addSel( Gen1MoveAsyncSel, "Gen1MoveAsyncSel" );
addSel( Gen1MoveSpanSel, "Gen1MoveSpanSel" );
addSel( Gen2ModeSel, "Gen2ModeSel" );
addSel( Gen2bModeSel, "Gen2bModeSel" );
addSel( Gen2cModeSel, "Gen2cModeSel" );
addSel( Gen2dModeSel, "Gen2dModeSel" );
addSel( Gen3ModeSel, "Gen3ModeSel" );
addSel( Gen4MixFacSel, "Gen4MixFacSel" );
addSel( Gen5bModeSel, "Gen5bModeSel" );
addSel( Gen7PowFacSel, "Gen7PowFacSel" );
addSel( Gen8ModeSel, "Gen8ModeSel" );
addSel( Gen8NumSel, "Gen8NumSel" );
addSel( Gen8SpanSel[ 0 ], "Gen8SpanSel[ 0 ]" );
addSel( Gen8SpanSel[ 1 ], "Gen8SpanSel[ 1 ]" );
}
/**
* Function updates dimensionality of *this object. Function does nothing
* if dimensionality has not changed since the last call. This function
* should be called at least once before calling the init() function.
*
* @param aParamCount The number of parameters being optimized.
* @param PopSize0 The number of elements in population to use. If set to
* 0 or negative, the default formula will be used.
*/
void updateDims( const int aParamCount, const int PopSize0 = 0 )
{
const int aPopSize = ( PopSize0 > 0 ? PopSize0 :
calcPopSizeBiteOpt( aParamCount ));
if( aParamCount == ParamCount && aPopSize == PopSize )
{
return;
}
initBuffers( aParamCount, aPopSize );
setParPopCount( 5 );
ParOpt.updateDims( aParamCount, 11 + aPopSize / 3 );
ParOptPop.initBuffers( aParamCount, aPopSize );
ParOpt2.updateDims( aParamCount, aPopSize );
ParOpt2Pop.initBuffers( aParamCount, aPopSize );
OldPops[ 0 ].initBuffers( aParamCount, aPopSize );
OldPops[ 1 ].initBuffers( aParamCount, aPopSize );
}
/**
* Function initializes *this optimizer. Does not perform objective
* function evaluations.
*
* @param rnd Random number generator.
* @param InitParams If not NULL, initial parameter vector, also used as
* centroid for initial population vectors.
* @param InitRadius Initial radius, multiplier relative to the default
* sigma value.
*/
void init( CBiteRnd& rnd, const double* const InitParams = NULL,
const double InitRadius = 1.0 )
{
initCommonVars( rnd );
StartSD = 0.25 * InitRadius;
setStartParams( InitParams );
ParOpt.init( rnd, InitParams, InitRadius );
ParOpt2.init( rnd, InitParams, InitRadius );
UseParOpt = 0;
ParOptPop.resetCurPopPos();
ParOpt2Pop.resetCurPopPos();
OldPops[ 0 ].resetCurPopPos();
OldPops[ 1 ].resetCurPopPos();
}
/**
* Function performs the parameter optimization iteration that involves 1
* objective function evaluation.
*
* @param rnd Random number generator.
* @param PushOpt Optimizer where the recently obtained solution should be
* "pushed", used for deep optimization algorithm.
* @return The number of non-improving iterations so far. A high value
* means optimizer has reached an optimization plateau. The suggested
* threshold value is ParamCount * 128. When this value was reached, the
* probability of plateau is high. This value, however, should not be
* solely relied upon when considering a stopping criteria: a hard
* iteration limit should be always used as in some cases convergence time
* may be very high with small, but frequent improving steps. This value
* is best used to allocate iteration budget between optimization attempts
* more efficiently.
*/
int optimize( CBiteRnd& rnd, CBiteOpt* const PushOpt = NULL )
{
int i;
if( DoInitEvals )
{
ptype* const Params = getCurParams();
genInitParams( rnd, Params );
NewCosts[ 0 ] = fixCostNaN( optcost( NewValues ));
updateBestCost( NewCosts[ 0 ], NewValues,
updatePop( NewCosts[ 0 ], Params ));
if( CurPopPos == PopSize )
{
updateCentroid();
for( i = 0; i < ParPopCount; i++ )
{
ParPops[ i ] -> copy( *this );
}
DoInitEvals = false;
}
return( 0 );
}
DoEval = true;
const int SelMethod = select( MethodSel, rnd );
if( SelMethod == 0 )
{
generateSol2( rnd );
}
else
if( SelMethod == 1 )
{
const int SelM1 = select( M1Sel, rnd );
if( SelM1 == 0 )
{
const int SelM1A = select( M1ASel, rnd );
if( SelM1A == 0 )
{
generateSol2b( rnd );
}
else
if( SelM1A == 1 )
{
generateSol2c( rnd );
}
else
{
generateSol2d( rnd );
}
}
else
if( SelM1 == 1 )
{
const int SelM1B = select( M1BSel, rnd );
if( SelM1B == 0 )
{
generateSol4( rnd );
}
else
if( SelM1B == 1 )
{
generateSol5b( rnd );
}
else
if( SelM1B == 2 )
{
generateSol5c( rnd );
}
else
{
generateSol13( rnd );
}
}
else
if( SelM1 == 2 )
{
const int SelM1C = select( M1CSel, rnd );
if( SelM1C == 0 )
{
generateSol5( rnd );
}
else
if( SelM1C == 1 )
{
generateSol10( rnd );
}
else
{
generateSol11( rnd );
}
}
else
{
generateSol6( rnd );
}
}
else
if( SelMethod == 2 )
{
if( select( M2Sel, rnd ))
{
generateSol1( rnd );
}
else
{
const int SelM2B = select( M2BSel, rnd );
if( SelM2B == 0 )
{
generateSol3( rnd );
}
else
if( SelM2B == 1 )
{
generateSol7( rnd );
}
else
if( SelM2B == 2 )
{
generateSol8( rnd );
}
else
if( SelM2B == 3 )
{
generateSol9( rnd );
}
else
{
generateSol12( rnd );
}
}
}
else
{
generateSolPar( rnd );
}
if( DoEval )
{
// Evaluate objective function with new parameters, if the
// solution was not provided by the parallel optimizer.
// Wrap parameter values so that they stay in the [0; 1] range.
for( i = 0; i < ParamCount; i++ )
{
TmpParams[ i ] = wrapParam( rnd, TmpParams[ i ]);
NewValues[ i ] = getRealValue( TmpParams, i );
}
NewCosts[ 0 ] = fixCostNaN( optcost( NewValues ));
LastCosts = NewCosts;
LastValues = NewValues;
}
const int p = updatePop( LastCosts[ 0 ], TmpParams, true, 3 );
if( p > CurPopSize1 )
{
// Upper bound cost constraint check failed, reject this solution.
applySelsDecr( rnd );
StallCount++;
if( DoEval && CurPopSize < PopSize )
{
if( select( PopChangeIncrSel, rnd ))
{
// Increase population size on fail.
incrCurPopSize();
}
}
}
else
{
updateBestCost( LastCosts[ 0 ], LastValues, p );
applySelsIncr( rnd, 1.0 - p * CurPopSizeI );
StallCount = 0;
ptype* const OldParams = getParamsOrdered( CurPopSize1 );
if( rnd.get() < ParamCountI )
{
OldPops[ 0 ].updatePop( *getObjPtr( OldParams ), OldParams,
false );
}
if( rnd.get() < 2.0 * ParamCountI )
{
OldPops[ 1 ].updatePop( *getObjPtr( OldParams ), OldParams,
false );
}
if( PushOpt != NULL && PushOpt != this &&
!PushOpt -> DoInitEvals && p > 1 )
{
PushOpt -> updatePop( LastCosts[ 0 ], TmpParams, true, 3 );
PushOpt -> updateParPop( LastCosts[ 0 ], TmpParams );
}
if( DoEval && CurPopSize > PopSize / 2 )
{
if( select( PopChangeDecrSel, rnd ))
{
// Decrease population size on success.
decrCurPopSize();
}
}
}
// "Diverging populations" technique.
updateParPop( LastCosts[ 0 ], TmpParams );
return( StallCount );
}
protected:
CBiteSel< 4 > MethodSel; ///< Population generator 4-method selector.
CBiteSel< 4 > M1Sel; ///< Method 1's sub-method selector.
CBiteSel< 3 > M1ASel; ///< Method 1's sub-sub-method A selector.
CBiteSel< 4 > M1BSel; ///< Method 1's sub-sub-method B selector.
CBiteSel< 3 > M1CSel; ///< Method 1's sub-sub-method C selector.
CBiteSel< 2 > M2Sel; ///< Method 2's sub-method selector.
CBiteSel< 5 > M2BSel; ///< Method 2's sub-sub-method B selector.
CBiteSel< 2 > PopChangeIncrSel; ///< Population size change increase
///< selector.
CBiteSel< 2 > PopChangeDecrSel; ///< Population size change decrease
///< selector.
CBiteSel< 2 > ParOpt2Sel; ///< Parallel optimizer 2 use selector.
CBiteSel< 2 > ParPopPSel[ 8 ]; ///< Parallel population use
///< probability selectors.
CBiteSel< 2 > AltPopPSel; ///< Alternative population use selector.
CBiteSel< 2 > AltPopSel[ 4 ]; ///< Alternative population type use
///< selectors.
CBiteSel< 2 > OldPopSel; ///< Old population use selector.
CBiteSel< 4 > MinSolPwrSel[ 4 ]; ///< Power factor selectors, for
///< least-cost population index selection.
CBiteSel< 4 > MinSolMulSel[ 4 ]; ///< Multiplier selectors, for
///< least-cost population index selection.
CBiteSel< 2 > Gen1AllpSel; ///< Generator method 1's Allp selector.
CBiteSel< 2 > Gen1MoveAsyncSel; ///< Generator method 1's Move async
///< selector.
CBiteSel< 4 > Gen1MoveSpanSel; ///< Generator method 1's Move span
///< selector.
CBiteSel< 2 > Gen2ModeSel; ///< Generator method 2's Mode selector.
CBiteSel< 2 > Gen2bModeSel; ///< Generator method 2b's Mode selector.
CBiteSel< 2 > Gen2cModeSel; ///< Generator method 2c's Mode selector.
CBiteSel< 2 > Gen2dModeSel; ///< Generator method 2d's Mode selector.
CBiteSel< 4 > Gen3ModeSel; ///< Generator method 3's Mode selector.
CBiteSel< 4 > Gen4MixFacSel; ///< Generator method 4's mixing count
///< selector.
CBiteSel< 2 > Gen5bModeSel; ///< Generator method 5b's Mode selector.
CBiteSel< 4 > Gen7PowFacSel; ///< Generator method 7's Power selector.
CBiteSel< 2 > Gen8ModeSel; ///< Generator method 8's mode selector.
CBiteSel< 4 > Gen8NumSel; ///< Generator method 8's NumSols selector.
CBiteSel< 4 > Gen8SpanSel[ 2 ]; ///< Generator method 8's random span
///< selectors.
CBitePop OldPops[ 2 ]; ///< Populations of older solutions, updated
///< probabilistically.
bool DoEval; ///< Temporary variable which equals to "true" if the
///< newly-generated solution should be evaluated via the optcost()
///< function.
CBiteOptOwned< CSpherOpt > ParOpt; ///< Parallel optimizer.
CBitePop ParOptPop; ///< Population of parallel optimizer's solutions.
///< Includes only its solutions.
CBiteOptOwned< CMiniBiteOpt > ParOpt2; ///< Parallel optimizer 2.
CBitePop ParOpt2Pop; ///< Population of parallel optimizer 2's solutions.
///< Includes only its solutions.
int UseParOpt; ///< Parallel optimizer currently being in use.
/**
* Function updates an appropriate parallel population.
*
* @param UpdCost Cost of the new solution.
* @param UpdParams New parameter values.
*/
void updateParPop( const double UpdCost, const ptype* const UpdParams )
{
const int p = getMinDistParPop( UpdCost, UpdParams );
if( p >= 0 )
{
ParPops[ p ] -> updatePop( UpdCost, UpdParams, true, 0 );
}
}
/**
* Function selects a parallel population to use for solution generation.
* With certain probability, *this object's own population will be
* returned instead of parallel population.
*
* @param gi Solution generator index (0-7).
* @param rnd PRNG object.
*/
CBitePop& selectParPop( const int gi, CBiteRnd& rnd )
{
if( select( ParPopPSel[ gi ], rnd ))
{
return( *ParPops[ rnd.getInt( ParPopCount )]);
}
return( *this );
}
/**
* Function selects an alternative, parallel optimizer's, population, to
* use in some solution generators.
*
* @param gi Solution generator index (0-3).
* @param rnd PRNG object.
*/
CBitePop& selectAltPop( const int gi, CBiteRnd& rnd )
{
if( select( AltPopPSel, rnd ))
{
if( select( AltPopSel[ gi ], rnd ))
{
if( ParOptPop.getCurPopPos() >= CurPopSize )
{
return( ParOptPop );
}
}
else
{
if( ParOpt2Pop.getCurPopPos() >= CurPopSize )
{
return( ParOpt2Pop );
}
}
}
return( *this );
}
/**
* Function returns a dynamically-selected minimal population index, used
* in some solution generation methods.
*
* @param gi Solution generator index (0-3).
* @param rnd PRNG object.
* @param ps Population size.
*/
int getMinSolIndex( const int gi, CBiteRnd& rnd, const int ps )
{
static const double pp[ 4 ] = { 0.05, 0.125, 0.25, 0.5 };
const double r = ps * rnd.getPow( ps *
pp[ select( MinSolPwrSel[ gi ], rnd )]);
static const double rm[ 4 ] = { 0.0, 0.125, 0.25, 0.5 };
return( (int) ( r * rm[ select( MinSolMulSel[ gi ], rnd )]));
}
/**
* The original "bitmask inversion with random move" solution generator.
* Most of the time adjusts only a single parameter of a better solution,
* yet manages to produce excellent "reference points".
*/
void generateSol1( CBiteRnd& rnd )
{
ptype* const Params = TmpParams;
const CBitePop& ParPop = selectParPop( 0, rnd );
const int ParPopSize = ParPop.getCurPopSize();
copyParams( Params, ParPop.getParamsOrdered(
getMinSolIndex( 0, rnd, ParPopSize )));
// Select a single random parameter or all parameters for further
// operations.
int a;
int b;
bool DoAllp = false;
if( rnd.get() < 1.8 * ParamCountI )
{
if( select( Gen1AllpSel, rnd ))
{
DoAllp = true;
}
}
if( DoAllp )
{
a = 0;
b = ParamCount;
}
else
{
a = rnd.getInt( ParamCount );
b = a + 1;
}
// Bitmask inversion operation, works as the main "driver" of
// optimization process.
const double r1 = rnd.get();
const double r12 = r1 * r1;
const int ims = (int) ( r12 * r12 * 48.0 );
const ptype imask = ( ims > IntMantBits ? 0 : IntMantMask >> ims );
const int im2s = rnd.getSqrInt( 96 );
const ptype imask2 = ( im2s > IntMantBits ? 0 : IntMantMask >> im2s );
const int si1 = (int) ( r1 * r12 * ParPopSize );
const ptype* const rp1 = ParPop.getParamsOrdered( si1 );
int i;
for( i = a; i < b; i++ )
{
Params[ i ] = (( Params[ i ] ^ imask ) +
( rp1[ i ] ^ imask2 )) >> 1;
}
if( rnd.get() < 1.0 - ParamCountI )
{
const ptype* const rp2 = ParPop.getParamsOrdered(
rnd.getSqrInt( ParPopSize ));
if( rnd.get() < sqrt( ParamCountI ))
{
if( select( Gen1MoveAsyncSel, rnd ))
{
a = 0;
b = ParamCount;
}
}
// Random move around a random previous solution vector.
static const double SpanMults[ 4 ] = { 0.5, 1.5, 2.0, 2.5 };
const double m = SpanMults[ select( Gen1MoveSpanSel, rnd )];
const double m1 = rnd.getTPDF() * m;
const double m2 = rnd.getTPDF() * m;
for( i = a; i < b; i++ )
{
Params[ i ] += (ptype) (( rp2[ i ] - Params[ i ]) * m1 );
Params[ i ] += (ptype) (( rp2[ i ] - Params[ i ]) * m2 );
}
}
}
/**
* The "Differential Evolution"-based solution generator. Note that
* compared to a usual DE, this generator does not use crossover, and
* it uses one, or an average of two best solutions as the base.
*/
void generateSol2( CBiteRnd& rnd )
{
ptype* const Params = TmpParams;
const CBitePop& ParPop = *this;
const int ParPopSize = ParPop.getCurPopSize();
const int ParPopSize1 = ParPopSize - 1;
const int si1 = getMinSolIndex( 1, rnd, ParPopSize );
const ptype* const rp1 = ParPop.getParamsOrdered( si1 );
const ptype* const rp3 = ParPop.getParamsOrdered( ParPopSize1 - si1 );
const int si2 = 1 + rnd.getInt( ParPopSize1 );
const ptype* const rp2 = ParPop.getParamsOrdered( si2 );
const int si4 = rnd.getSqrInt( ParPopSize );
const ptype* const rp4 = ParPop.getParamsOrdered( si4 );
const ptype* const rp5 = ParPop.getParamsOrdered( ParPopSize1 - si4 );
// The "step in the right direction" (Differential Evolution
// "mutation") operation towards the best (minimal) and away from
// the worst (maximal) parameter vector, plus a difference of two
// random vectors.
const int Mode = select( Gen2ModeSel, rnd );
int i;
if( Mode == 0 )
{
for( i = 0; i < ParamCount; i++ )
{
Params[ i ] = rp1[ i ] + ((( rp2[ i ] - rp3[ i ]) +
( rp4[ i ] - rp5[ i ])) >> 1 );
}
}
else
{
const ptype* const rp1b = ParPop.getParamsOrdered(
rnd.getSqrInt( ParPopSize ));
for( i = 0; i < ParamCount; i++ )
{
Params[ i ] = (( rp1[ i ] + rp1b[ i ]) +
( rp2[ i ] - rp3[ i ]) + ( rp4[ i ] - rp5[ i ])) >> 1;
}
}
}
/**
* An alternative "Differential Evolution"-based solution generator.
*/
void generateSol2b( CBiteRnd& rnd )
{
ptype* const Params = TmpParams;
// rand/2/none DE-alike mutation.
const int si1 = getMinSolIndex( 2, rnd, CurPopSize );
const ptype* const rp1 = getParamsOrdered( si1 );
const int si2 = rnd.getInt( CurPopSize );
const ptype* const rp2 = getParamsOrdered( si2 );
const ptype* const rp3 = getParamsOrdered( CurPopSize1 - si2 );
const CBitePop& AltPop = selectAltPop( 0, rnd );
const int si4 = rnd.getInt( CurPopSize );
const ptype* const rp4 = AltPop.getParamsOrdered( si4 );
const ptype* const rp5 = AltPop.getParamsOrdered( CurPopSize1 - si4 );
const int Mode = select( Gen2bModeSel, rnd );
int i;
if( Mode == 0 )
{
for( i = 0; i < ParamCount; i++ )
{
Params[ i ] = rp1[ i ] + ((( rp2[ i ] - rp3[ i ]) +
( rp4[ i ] - rp5[ i ])) >> 1 );
}
}
else
{
const ptype* const rp1b = getParamsOrdered(
rnd.getSqrInt( CurPopSize ));
for( i = 0; i < ParamCount; i++ )
{
Params[ i ] = ( rp1[ i ] + rp1b[ i ] +
( rp2[ i ] - rp3[ i ]) + ( rp4[ i ] - rp5[ i ])) >> 1;
}
}
}
/**
* "Differential Evolution"-based solution generator, almost an exact
* replica of the CDEOpt optimizer.
*/
void generateSol2c( CBiteRnd& rnd )
{
ptype* const Params = TmpParams;
zeroParams( Params );
const int si1 = rnd.getPowInt( 4.0, CurPopSize / 2 );
const ptype* const rp1 = getParamsOrdered( si1 );
const int PairCount = 3;
const int pc = 1 + 2 * PairCount;
int PopIdx[ pc ];
PopIdx[ 0 ] = si1;
int pp = 1;
int i;
int j;
if( CurPopSize1 <= pc )
{
while( pp < pc )
{
PopIdx[ pp ] = rnd.getInt( CurPopSize );
pp++;
}
}
else
{
while( pp < pc )
{
const int sii = rnd.getInt( CurPopSize );
for( j = 0; j < pp; j++ )
{
if( PopIdx[ j ] == sii )
{
break;
}
}
if( j == pp )
{
PopIdx[ pp ] = sii;
pp++;
}
}
}
const ptype* const rp2 = getParamsOrdered( PopIdx[ 1 ]);
const ptype* const rp3 = getParamsOrdered( PopIdx[ 2 ]);
const ptype* const rp4 = getParamsOrdered( PopIdx[ 3 ]);
const ptype* const rp5 = getParamsOrdered( PopIdx[ 4 ]);
const ptype* const rp6 = getParamsOrdered( PopIdx[ 5 ]);
const ptype* const rp7 = getParamsOrdered( PopIdx[ 6 ]);
for( i = 0; i < ParamCount; i++ )
{
Params[ i ] = ( rp2[ i ] - rp3[ i ]) + ( rp4[ i ] - rp5[ i ]) +
( rp6[ i ] - rp7[ i ]);
}
if( rnd.getBit() && rnd.getBit() )
{
const int k = rnd.getInt( ParamCount );
// Produce sparsely-random bit-strings.
const ptype v1 = rnd.getRaw() & rnd.getRaw() & rnd.getRaw() &
rnd.getRaw() & rnd.getRaw() & IntMantMask;
const ptype v2 = rnd.getRaw() & rnd.getRaw() & rnd.getRaw() &
rnd.getRaw() & rnd.getRaw() & IntMantMask;
Params[ k ] += v1 - v2; // Apply in TPDF manner.
}
const int Mode = select( Gen2cModeSel, rnd );
if( Mode == 0 )
{
int si2 = si1 + rnd.getBit() * 2 - 1;
if( si2 < 0 )
{
si2 = 1;
}
const ptype* const rp1b = getParamsOrdered( si2 );
for( i = 0; i < ParamCount; i++ )
{
Params[ i ] = ( rp1[ i ] + rp1b[ i ] + Params[ i ]) >> 1;
}
}
else
{
for( i = 0; i < ParamCount; i++ )
{
Params[ i ] = rp1[ i ] + ( Params[ i ] >> 1 );
}
}
}
/**
* An alternative "Differential Evolution"-based solution generator that
* uses "OldPop" population.
*/
void generateSol2d( CBiteRnd& rnd )
{
const CBitePop& OldPop = OldPops[ select( OldPopSel, rnd )];
if( OldPop.getCurPopPos() < 3 )
{
generateSol2c( rnd );
return;
}
ptype* const Params = TmpParams;
const ptype* const rp1 = getParamsOrdered(
rnd.getSqrInt( CurPopSize ));
const ptype* const rp2 = getParamsOrdered(
rnd.getInt( CurPopSize ));
const ptype* const rp3 = OldPop.getParamsOrdered(
rnd.getInt( OldPop.getCurPopPos() ));
const int Mode = select( Gen2dModeSel, rnd );
int i;
if( Mode == 0 )
{
for( i = 0; i < ParamCount; i++ )
{
Params[ i ] = rp1[ i ] + (( rp2[ i ] - rp3[ i ]) >> 1 );
}
}
else
{
const ptype* const rp1b = getParamsOrdered(
rnd.getSqrInt( CurPopSize ));
for( i = 0; i < ParamCount; i++ )
{
Params[ i ] = (( rp1[ i ] + rp1b[ i ]) +
( rp2[ i ] - rp3[ i ])) >> 1;
}
}
}
/**
* "Centroid mix with DE" solution generator, works well for convex
* functions. For DE operation, uses a better solution and a random
* worse solution.
*/
void generateSol3( CBiteRnd& rnd )
{
ptype* const Params = TmpParams;
const CBitePop& ParPop = selectParPop( 2, rnd );
const int ParPopSize = ParPop.getCurPopSize();
const ptype* const rp1 = ParPop.getParamsOrdered(
getMinSolIndex( 3, rnd, ParPopSize ));
const ptype* const rp2 = ParPop.getParamsOrdered(
rnd.getSqrIntInv( ParPopSize ));
const int Mode = select( Gen3ModeSel, rnd );
int i;
if( Mode == 0 )
{
for( i = 0; i < ParamCount; i++ )
{
Params[ i ] = rp1[ i ] + ( rp1[ i ] - rp2[ i ]);
}
}
else
{
static const double CentProb[ 4 ] = { 0.0, 0.25, 0.5, 0.75 };
const double p = CentProb[ Mode ];
const ptype* const cp = getCentroid();
for( i = 0; i < ParamCount; i++ )
{
Params[ i ] = ( rnd.get() < p ? cp[ i ] :
rp1[ i ] + ( rp1[ i ] - rp2[ i ]));
}
}
}
/**
* "Entropy bit mixing"-based solution generator. Performs crossing-over
* of an odd number (this is important) of random solutions via XOR
* operation. Slightly less effective than the DE-based mixing, but makes
* the optimization method more diverse overall.
*/
void generateSol4( CBiteRnd& rnd )
{
ptype* const Params = TmpParams;
const CBitePop* UsePops[ 2 ];
UsePops[ 0 ] = &selectAltPop( 1, rnd );
UsePops[ 1 ] = &selectParPop( 3, rnd );
int UseSize[ 2 ];
UseSize[ 0 ] = CurPopSize;
UseSize[ 1 ] = UsePops[ 1 ] -> getCurPopSize();
const int km = 3 + ( select( Gen4MixFacSel, rnd ) << 1 );
int p = rnd.getBit();
const ptype* rp1 = UsePops[ p ] -> getParamsOrdered(
rnd.getSqrInt( UseSize[ p ]));
copyParams( Params, rp1 );
int k;
for( k = 1; k < km; k++ )
{
p = rnd.getBit();
rp1 = UsePops[ p ] -> getParamsOrdered(
rnd.getSqrInt( UseSize[ p ]));
int i;
for( i = 0; i < ParamCount; i++ )
{
Params[ i ] ^= rp1[ i ];
}
}
// Simple XOR randomize.
int b = rnd.getSqrInt( 54 );