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// @(#)root/tmva $Id$
// Author: Peter Speckmayer
/**********************************************************************************
* Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
* Package: TMVA *
* Class : GeneticAlgorithm *
* Web : http://tmva.sourceforge.net *
* *
* Description: *
* Base definition for genetic algorithm *
* *
* Authors (alphabetical): *
* Peter Speckmayer <speckmay@mail.cern.ch> - CERN, Switzerland *
* *
* Copyright (c) 2005: *
* CERN, Switzerland *
* MPI-K Heidelberg, Germany *
* *
* Redistribution and use in source and binary forms, with or without *
* modification, are permitted according to the terms listed in LICENSE *
* (http://tmva.sourceforge.net/LICENSE) *
**********************************************************************************/
#ifndef ROOT_TMVA_GeneticAlgorithm
#define ROOT_TMVA_GeneticAlgorithm
//////////////////////////////////////////////////////////////////////////
// //
// GeneticAlgorithm //
// //
// Base definition for genetic algorithm //
// //
//////////////////////////////////////////////////////////////////////////
#include <vector>
#include <deque>
#include <iosfwd>
#include "TMVA/IFitterTarget.h"
#include "TMVA/GeneticPopulation.h"
#include "TMVA/Types.h"
namespace TMVA {
class IFitterTarget;
class Interval;
class MsgLogger;
class GeneticAlgorithm {
public:
GeneticAlgorithm( IFitterTarget& target, Int_t populationSize,
const std::vector<TMVA::Interval*>& ranges, UInt_t seed = 0 );
virtual ~GeneticAlgorithm();
void Init();
virtual Bool_t HasConverged(Int_t steps = 10, Double_t ratio = 0.1);
virtual Double_t SpreadControl(Int_t steps, Int_t ofSteps,
Double_t factor);
virtual Double_t NewFitness(Double_t oldValue, Double_t newValue);
virtual Double_t CalculateFitness();
virtual void Evolution();
GeneticPopulation& GetGeneticPopulation() { return fPopulation; }
Double_t GetSpread() const { return fSpread; }
void SetSpread(Double_t s) { fSpread = s; }
void SetMakeCopies(Bool_t s) { fMakeCopies = s; }
Bool_t GetMakeCopies() { return fMakeCopies; }
Int_t fConvCounter; // converging? ... keeps track of the number of improvements
protected:
IFitterTarget& fFitterTarget; // the fitter target
Double_t fConvValue; // keeps track of the quantity of improvement
// spread-control (stepsize)
// successList keeps track of the improvements to be able
std::deque<Int_t> fSuccessList; // to adjust the stepSize
Double_t fLastResult; // remembers the last obtained result (for internal use)
Double_t fSpread; // regulates the spread of the value change at mutation (sigma)
Bool_t fMirror; // new values for mutation are mirror-mapped if outside of constraints
Bool_t fFirstTime; // if true its the first time, so no evolution yet
Bool_t fMakeCopies; // if true, the population will make copies of the first individuals
// avoid for speed performance.
Int_t fPopulationSize; // the size of the population
const std::vector<TMVA::Interval*>& fRanges; // parameter ranges
GeneticPopulation fPopulation; // contains and controls the "individual"
Double_t fBestFitness;
mutable MsgLogger* fLogger; // message logger
MsgLogger& Log() const { return *fLogger; }
ClassDef(GeneticAlgorithm, 0); // Genetic algorithm controller
};
} // namespace TMVA
#endif
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