Base definition for genetic algorithm.

Definition at line 50 of file GeneticAlgorithm.h.

Public Member Functions
	GeneticAlgorithm (IFitterTarget &target, Int_t populationSize, const std::vector< TMVA::Interval * > &ranges, UInt_t seed=0)
	Constructor.

virtual	~GeneticAlgorithm ()

virtual Double_t	CalculateFitness ()
	starts the evaluation of the fitness of all different individuals of the population.

virtual void	Evolution ()
	this function is called from "init" and controls the evolution of the individuals.

GeneticPopulation &	GetGeneticPopulation ()

Bool_t	GetMakeCopies ()

Double_t	GetSpread () const

virtual Bool_t	HasConverged (Int_t steps=10, Double_t ratio=0.1)
	gives back true if the last "steps" steps have lead to an improvement of the "fitness" of the "individuals" of at least "improvement"

void	Init ()
	calls evolution, but if it is not the first time.

virtual Double_t	NewFitness (Double_t oldValue, Double_t newValue)
	if the "fitnessFunction" is called multiple times for one set of factors (because i.e.

void	SetMakeCopies (Bool_t s)

void	SetSpread (Double_t s)

virtual Double_t	SpreadControl (Int_t steps, Int_t ofSteps, Double_t factor)
	this function provides the ability to change the stepSize of a mutation according to the success of the last generations.

Public Attributes
Int_t	fConvCounter

Protected Member Functions
MsgLogger &	Log () const

Protected Attributes
Double_t	fBestFitness

Double_t	fConvValue

Bool_t	fFirstTime

IFitterTarget &	fFitterTarget

Double_t	fLastResult

MsgLogger *	fLogger

Bool_t	fMakeCopies

Bool_t	fMirror

GeneticPopulation	fPopulation

Int_t	fPopulationSize

const std::vector< TMVA::Interval * > &	fRanges

Double_t	fSpread

std::deque< Int_t >	fSuccessList

#include <TMVA/GeneticAlgorithm.h>

Constructor & Destructor Documentation

◆ GeneticAlgorithm()

TMVA::GeneticAlgorithm::GeneticAlgorithm	(	IFitterTarget &	target,
		Int_t	populationSize,
		const std::vector< TMVA::Interval * > &	ranges,
		UInt_t	seed = `0`
	)

Constructor.

Parameters:

int populationSize : defines the number of "Individuals" which are created and tested within one Generation (Iteration of the Evolution)
std::vector<TMVA::Interval*> ranges : Interval holds the information of an interval, where the GetMin gets the low and GetMax gets the high constraint of the variable the size of "ranges" is the number of coefficients which are optimised Purpose:

Creates a random population with individuals of the size ranges.size()

Definition at line 69 of file GeneticAlgorithm.cxx.

◆ ~GeneticAlgorithm()

TMVA::GeneticAlgorithm::~GeneticAlgorithm ( )

virtual

Definition at line 88 of file GeneticAlgorithm.cxx.

Member Function Documentation

◆ CalculateFitness()

Double_t TMVA::GeneticAlgorithm::CalculateFitness ( )

virtual

starts the evaluation of the fitness of all different individuals of the population.

this function calls implicitly (many times) the "fitnessFunction" which has been overridden by the user.

Definition at line 135 of file GeneticAlgorithm.cxx.

◆ Evolution()

void TMVA::GeneticAlgorithm::Evolution ( )

virtual

this function is called from "init" and controls the evolution of the individuals.

The function can be overridden to change the parameters for mutation rate sexual reproduction and so on.

Definition at line 190 of file GeneticAlgorithm.cxx.

◆ GetGeneticPopulation()

GeneticPopulation & TMVA::GeneticAlgorithm::GetGeneticPopulation ( )

inline

Definition at line 67 of file GeneticAlgorithm.h.

◆ GetMakeCopies()

Bool_t TMVA::GeneticAlgorithm::GetMakeCopies ( )

inline

Definition at line 73 of file GeneticAlgorithm.h.

◆ GetSpread()

Double_t TMVA::GeneticAlgorithm::GetSpread ( ) const

inline

Definition at line 69 of file GeneticAlgorithm.h.

◆ HasConverged()

Bool_t TMVA::GeneticAlgorithm::HasConverged	(	Int_t	steps = `10`,
		Double_t	improvement = `0.1`
	)

virtual

gives back true if the last "steps" steps have lead to an improvement of the "fitness" of the "individuals" of at least "improvement"

this gives a simple measure of if the fitness of the individuals is converging and no major improvement is to be expected soon.

Definition at line 260 of file GeneticAlgorithm.cxx.

◆ Init()

void TMVA::GeneticAlgorithm::Init ( )

calls evolution, but if it is not the first time.

If it's the first time, the random population created by the constructor is still not evaluated, .. therefore we wait for the second time init is called.

Definition at line 101 of file GeneticAlgorithm.cxx.

◆ Log()

MsgLogger & TMVA::GeneticAlgorithm::Log ( ) const

inlineprotected

Definition at line 102 of file GeneticAlgorithm.h.

◆ NewFitness()

Double_t TMVA::GeneticAlgorithm::NewFitness	(	Double_t	oldValue,
		Double_t	newValue
	)

virtual

if the "fitnessFunction" is called multiple times for one set of factors (because i.e.

each event of a TTree has to be assessed with each set of Factors proposed by the Genetic Algorithm) the value of the current calculation has to be added(? or else) to the value obtained up to now. example: some chi-square is calculated for every event, after every event the new chi-square (newValue) has to be simply added to the oldValue.

this function has to be overridden eventually it might contain only the following return statement. return oldValue + newValue;

Definition at line 123 of file GeneticAlgorithm.cxx.

◆ SetMakeCopies()

void TMVA::GeneticAlgorithm::SetMakeCopies ( Bool_t s )

inline

Definition at line 72 of file GeneticAlgorithm.h.

◆ SetSpread()

void TMVA::GeneticAlgorithm::SetSpread ( Double_t s )

inline

Definition at line 70 of file GeneticAlgorithm.h.

◆ SpreadControl()

Double_t TMVA::GeneticAlgorithm::SpreadControl	(	Int_t	ofSteps,
		Int_t	successSteps,
		Double_t	factor
	)

virtual

this function provides the ability to change the stepSize of a mutation according to the success of the last generations.

Parameters:

int ofSteps : = if OF the number of STEPS given in this variable (ofSteps)
int successSteps : >sucessSteps Generations could improve the result
double factor : than multiply the stepSize ( spread ) by this factor

(if ofSteps == successSteps nothing is changed, if ofSteps < successSteps, the spread is divided by the factor)

using this function one can increase the stepSize of the mutation when we have good success (to pass fast through the easy phase-space) and reduce the stepSize if we are in a difficult "territory" of the phase-space.

Definition at line 217 of file GeneticAlgorithm.cxx.

Member Data Documentation

◆ fBestFitness

Double_t TMVA::GeneticAlgorithm::fBestFitness

protected

Definition at line 99 of file GeneticAlgorithm.h.

◆ fConvCounter

Int_t TMVA::GeneticAlgorithm::fConvCounter

Definition at line 75 of file GeneticAlgorithm.h.

◆ fConvValue

Double_t TMVA::GeneticAlgorithm::fConvValue

protected

Definition at line 81 of file GeneticAlgorithm.h.

◆ fFirstTime

Bool_t TMVA::GeneticAlgorithm::fFirstTime

protected

Definition at line 91 of file GeneticAlgorithm.h.

◆ fFitterTarget

IFitterTarget& TMVA::GeneticAlgorithm::fFitterTarget

protected

Definition at line 79 of file GeneticAlgorithm.h.

◆ fLastResult

Double_t TMVA::GeneticAlgorithm::fLastResult

protected

Definition at line 87 of file GeneticAlgorithm.h.

◆ fLogger

MsgLogger* TMVA::GeneticAlgorithm::fLogger

mutableprotected

Definition at line 101 of file GeneticAlgorithm.h.

◆ fMakeCopies

Bool_t TMVA::GeneticAlgorithm::fMakeCopies

protected

Definition at line 92 of file GeneticAlgorithm.h.

◆ fMirror

Bool_t TMVA::GeneticAlgorithm::fMirror

protected

Definition at line 90 of file GeneticAlgorithm.h.

◆ fPopulation

GeneticPopulation TMVA::GeneticAlgorithm::fPopulation

protected

Definition at line 98 of file GeneticAlgorithm.h.

◆ fPopulationSize

Int_t TMVA::GeneticAlgorithm::fPopulationSize

protected

Definition at line 94 of file GeneticAlgorithm.h.

◆ fRanges

const std::vector<TMVA::Interval*>& TMVA::GeneticAlgorithm::fRanges

protected

Definition at line 96 of file GeneticAlgorithm.h.

◆ fSpread

Double_t TMVA::GeneticAlgorithm::fSpread

protected

Definition at line 89 of file GeneticAlgorithm.h.

◆ fSuccessList

std::deque<Int_t> TMVA::GeneticAlgorithm::fSuccessList

protected

Definition at line 86 of file GeneticAlgorithm.h.

Libraries for TMVA::GeneticAlgorithm:

[legend]

The documentation for this class was generated from the following files:

tmva/tmva/inc/TMVA/GeneticAlgorithm.h
tmva/tmva/src/GeneticAlgorithm.cxx

Public Member Functions

Public Attributes

Protected Member Functions

Protected Attributes

Constructor & Destructor Documentation

◆ GeneticAlgorithm()

◆ ~GeneticAlgorithm()

Member Function Documentation

◆ CalculateFitness()

◆ Evolution()

◆ GetGeneticPopulation()

◆ GetMakeCopies()

◆ GetSpread()

◆ HasConverged()

◆ Init()

◆ Log()

◆ NewFitness()

◆ SetMakeCopies()

◆ SetSpread()

◆ SpreadControl()

Member Data Documentation

◆ fBestFitness

◆ fConvCounter

◆ fConvValue

◆ fFirstTime

◆ fFitterTarget

◆ fLastResult

◆ fLogger

◆ fMakeCopies

◆ fMirror

◆ fPopulation

◆ fPopulationSize

◆ fRanges

◆ fSpread

◆ fSuccessList