# TMVAGAexample2¶

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, August 15, 2022 at 09:48 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;

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)) );
}
};

void exampleGA(){
std::cout << "\nEXAMPLE" << std::endl;
// define all the parameters by their minimum and maximum value
// in this example 3 parameters are defined.
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();

// 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

const TString name( "exampleGA" );
const TString opts( "PopSize=100:Steps=30" );

GeneticFitter mg( *myFitness, name, ranges, opts);
// mg.SetParameters( 4, 30, 200, 10,5, 0.95, 0.001 );

std::vector<Double_t> result;
Double_t estimator = mg.Run(result);

int n = 0;
for( std::vector<Double_t>::iterator it = result.begin(); it<result.end(); it++ ){
std::cout << "FACTOR " << n << " : " << (*it) << std::endl;
n++;
}

}

}

In :
cout << "Start Test TMVAGAexample" << endl
<< "========================" << endl
<< endl;

TMVA::exampleGA();

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

EXAMPLE
range: 0   15
range: 0   13
range: 0   5
FitterBase               : <GeneticFitter> Optimisation, please be patient ... (inaccurate progress timing for GA)
: Elapsed time: 0.0124 sec
FACTOR 0 : 15
FACTOR 1 : 13
FACTOR 2 : 0