# T M V A G Aexample¶

This executable gives an example of a very simple use of the genetic algorithm of TMVA

• Project : TMVA - a Root-integrated toolkit for multivariate data analysis
• Package : TMVA
• Executable: TMVAGAexample

Author: Andreas Hoecker
This notebook tutorial was automatically generated with ROOTBOOK-izer from the macro found in the ROOT repository on Monday, December 06, 2021 at 10:51 AM.

In :
%%cpp -d
#include <iostream> // Stream declarations
#include <vector>

#include "TMVA/GeneticAlgorithm.h"
#include "TMVA/GeneticFitter.h"
#include "TMVA/IFitterTarget.h"

using namespace std;

using namespace TMVA;

class MyFitness : public IFitterTarget {
public:
MyFitness() : IFitterTarget() {
}

// the fitness-function goes here
// the factors are optimized such that the return-value of this function is minimized
// take care!! the fitness-function must never fail, .. means: you have to prevent
// the function from reaching undefined values (such as x=0 for 1/x or so)
//
// HINT: to use INTEGER variables, it is sufficient to cast the "factor" in the fitness-function
// to (int). In this case the variable-range has to be chosen +1 ( to get 0..5, take Interval(0,6) )
// since the introduction of "Interval" ranges can be defined with a third parameter
// which gives the number of bins within the interval. With that technique discrete values
// can be achieved easier. The random selection out of this discrete numbers is completely uniform.
//
Double_t EstimatorFunction( std::vector<Double_t> & factors ){
//return (10.- (int)factors.at(0) *factors.at(1) + (int)factors.at(2));
return (10.- factors.at(0) *factors.at(1) + factors.at(2));

//return 100.- (10 + factors.at(1)) *factors.at(2)* TMath::Abs( TMath::Sin(factors.at(0)) );
}
};

class MyGA2nd : public GeneticAlgorithm {
public:
MyGA2nd( IFitterTarget& target, Int_t size, vector<Interval*>& ranges ) : GeneticAlgorithm(target,
size, ranges ){
}

// this method has to be activated if one wants to change the behaviour of the evolution
// works only with the head version
//void Evolution(){
//    fSexual = true;
//    if (fSexual) {
//       fPopulation.MakeCopies( 5 );
//       fPopulation.MakeChildren();
//       fPopulation.NextGeneration();

//       fPopulation.Mutate( 10, 3, kTRUE, fSpread, fMirror );
//       fPopulation.Mutate( 40, fPopulation.GetPopulationSize()*3/4 );
//    } else {
//       fPopulation.MakeCopies( 3 );
//       fPopulation.MakeMutants(100,true, 0.1, true);
//       fPopulation.NextGeneration();
//    }
// }
};

In :
std::cout << "Start Test TMVAGAexample" << std::endl
<< "========================" << std::endl
<< "\nEXAMPLE"              << std::endl;

Start Test TMVAGAexample
========================

EXAMPLE


Define all the parameters by their minimum and maximum value in this example 3 parameters are defined.

In :
vector<Interval*> ranges;
ranges.push_back( new Interval(0,15,30) );
ranges.push_back( new Interval(0,13) );
ranges.push_back( new Interval(0,5,3) );

for( std::vector<Interval*>::iterator it = ranges.begin(); it != ranges.end(); it++ ){
std::cout << " range: " << (*it)->GetMin() << "   " << (*it)->GetMax() << std::endl;
}

IFitterTarget* myFitness = new MyFitness();

 range: 0   15
range: 0   13
range: 0   5


Prepare the genetic algorithm with an initial population size of 20 mind: big population sizes will help in searching the domain space of the solution but you have to weight this out to the number of generations the extreme case of 1 generation and populationsize n is equal to a Monte Carlo calculation with n tries

In :
MyGA2nd mg( *myFitness, 100, ranges );


Mg.setparameters( 4, 30, 200, 10,5, 0.95, 0.001 );

In :
#define CONVSTEPS 20
#define CONVCRIT 0.0001
#define SCSTEPS 10
#define SCRATE 5
#define SCFACTOR 0.95

do {
// prepares the new generation and does evolution
mg.Init();

// assess the quality of the individuals
mg.CalculateFitness();

mg.GetGeneticPopulation().Print(0);
std::cout << "---" << std::endl;

// reduce the population size to the initially defined one
mg.GetGeneticPopulation().TrimPopulation();

// tricky thing: control the speed of how fast the "solution space" is searched through
// this function basically influences the sigma of a gaussian around the actual value
// of the parameter where the new value will be randomly thrown.
// when the number of improvements within the last SCSTEPS
// A) smaller than SCRATE: divide the preset sigma by SCFACTOR
// B) equal to SCRATE: do nothing
// C) greater than SCRATE: multiply the preset sigma by SCFACTOR
// if you don't know what to do, leave it unchanged or even delete this function call

} while (!mg.HasConverged( CONVSTEPS, CONVCRIT ));  // converged if: fitness-improvement < CONVCRIT within the last CONVSTEPS loops

GeneticGenes* genes = mg.GetGeneticPopulation().GetGenes( 0 );
std::vector<Double_t> gvec;
gvec = genes->GetFactors();
int n = 0;
for( std::vector<Double_t>::iterator it = gvec.begin(); it<gvec.end(); it++ ){
std::cout << "FACTOR " << n << " : " << (*it) << std::endl;
n++;
}

                         : fitness: -125.366    f_0: 11.3793     f_1: 12.3352     f_2: 5
---
: fitness: -167.682    f_0: 14.4828     f_1: 12.2685     f_2: 0
---
: fitness: -171.128    f_0: 15     f_1: 12.0752     f_2: 0
---
: fitness: -174.028    f_0: 15     f_1: 12.2685     f_2: 0
---
: fitness: -174.028    f_0: 15     f_1: 12.2685     f_2: 0
---
: fitness: -174.506    f_0: 15     f_1: 12.3004     f_2: 0
---
: fitness: -177.717    f_0: 14.4828     f_1: 12.9614     f_2: 0
---
: fitness: -184.421    f_0: 15     f_1: 12.9614     f_2: 0
---
: fitness: -184.421    f_0: 15     f_1: 12.9614     f_2: 0
---
: fitness: -184.421    f_0: 15     f_1: 12.9614     f_2: 0
---
: fitness: -184.674    f_0: 15     f_1: 12.9783     f_2: 0
---
: fitness: -184.674    f_0: 15     f_1: 12.9783     f_2: 0
---
: fitness: -184.674    f_0: 15     f_1: 12.9783     f_2: 0
---
: fitness: -184.674    f_0: 15     f_1: 12.9783     f_2: 0
---
: fitness: -184.674    f_0: 15     f_1: 12.9783     f_2: 0
---
: fitness: -184.674    f_0: 15     f_1: 12.9783     f_2: 0
---
: fitness: -184.674    f_0: 15     f_1: 12.9783     f_2: 0
---
: fitness: -184.9    f_0: 15     f_1: 12.9933     f_2: 0
---
: fitness: -184.9    f_0: 15     f_1: 12.9933     f_2: 0
---
: fitness: -184.9    f_0: 15     f_1: 12.9933     f_2: 0
---
: fitness: -184.9    f_0: 15     f_1: 12.9933     f_2: 0
---
: fitness: -184.907    f_0: 15     f_1: 12.9938     f_2: 0
---
: fitness: -184.907    f_0: 15     f_1: 12.9938     f_2: 0
---
: fitness: -184.907    f_0: 15     f_1: 12.9938     f_2: 0
---
: fitness: -184.907    f_0: 15     f_1: 12.9938     f_2: 0
---
: fitness: -184.907    f_0: 15     f_1: 12.9938     f_2: 0
---
: fitness: -184.907    f_0: 15     f_1: 12.9938     f_2: 0
---
: fitness: -184.907    f_0: 15     f_1: 12.9938     f_2: 0
---
: fitness: -184.92    f_0: 15     f_1: 12.9947     f_2: 0
---
: fitness: -184.92    f_0: 15     f_1: 12.9947     f_2: 0
---
: fitness: -184.92    f_0: 15     f_1: 12.9947     f_2: 0
---
: fitness: -184.92    f_0: 15     f_1: 12.9947     f_2: 0
---
: fitness: -184.944    f_0: 15     f_1: 12.9963     f_2: 0
---
: fitness: -184.944    f_0: 15     f_1: 12.9963     f_2: 0
---
: fitness: -184.944    f_0: 15     f_1: 12.9963     f_2: 0
---
: fitness: -184.944    f_0: 15     f_1: 12.9963     f_2: 0
---
: fitness: -184.944    f_0: 15     f_1: 12.9963     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
: fitness: -184.984    f_0: 15     f_1: 12.9989     f_2: 0
---
FACTOR 0 : 15
FACTOR 1 : 12.9989
FACTOR 2 : 0