library: libTMVA #include "GeneticPopulation.h" |

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Double_t | fCounterFitness | internal use |

Int_t | fPopulationSize | population size |

multimap<Double_t,GeneticGenes>* | fGenePool | the "genePool" where the individuals of the current generation are stored |

multimap<Double_t,GeneticGenes>* | fNewGenePool | the genePool where the offspring individuals are stored |

vector<TMVA::GeneticRange*,allocator<TMVA::GeneticRange*> > | fRanges | contains the ranges inbetween the values of the coefficients have to be |

multimap<double,TMVA::GeneticGenes,less<double>,allocator<pair<const double,TMVA::GeneticGenes> > >::iterator | fCounter | an internal counter |

TRandom* | fRandomGenerator | random Generator for this population |

TMVA::MsgLogger | fLogger | message logger |

Population definition for genetic algorithm _______________________________________________________________________

void CreatePopulation( Int_t size )

create a Population of individuals with the population size given in the parameter --> every coefficient gets a random value within the constraints provided by the user

void AddPopulation( TMVA::GeneticPopulation *strangers )

allows to connect two populations (using the same number of coefficients with the same ranges) this allows to calculate several populations on the same phase-space or on different parts of the same phase-space and combine them afterwards this improves the global outcome.

void TrimPopulation( )

after adding another population or givingHint, the true size of the population may be bigger than the size which was given at createPopulation trimPopulation should be called (if necessary) after having checked the individuals fitness with calculateFitness

void MakeChildren()

does what the name says,... it creates children out of members of the current generation children have a combination of the coefficients of their parents

TMVA::GeneticGenes MakeSex( TMVA::GeneticGenes male, TMVA::GeneticGenes female )

this function takes two individuals and produces offspring by mixing (recombining) their coefficients

void MakeMutants( Double_t probability, Bool_t near, Double_t spread, Bool_t mirror )

produces offspring which is are mutated versions of their parents Parameters: double probability : gives the probability (in percent) of a mutation of a coefficient bool near : if true, the mutation will produce a new coefficient which is "near" the old one (gaussian around the current value) double spread : if near==true, spread gives the sigma of the gaussian bool mirror : if the new value obtained would be outside of the given constraints the value is mapped between the constraints again. This can be done either by a kind of periodic boundary conditions or mirrored at the boundary. (mirror = true seems more "natural")

void Mutate( Double_t probability , Int_t startIndex, Bool_t near, Double_t spread, Bool_t mirror )

mutates the individuals in the genePool Parameters: double probability : gives the probability (in percent) of a mutation of a coefficient int startIndex : leaves unchanged (without mutation) the individuals which are better ranked than indicated by "startIndex". This means: if "startIndex==3", the first (and best) three individuals are not mutaded. This allows to preserve the best result of the current Generation for the next generation. bool near : if true, the mutation will produce a new coefficient which is "near" the old one (gaussian around the current value) double spread : if near==true, spread gives the sigma of the gaussian bool mirror : if the new value obtained would be outside of the given constraints the value is mapped between the constraints again. This can be done either by a kind of periodic boundary conditions or mirrored at the boundary. (mirror = true seems more "natural")

void AddFactor( Double_t from, Double_t to )

adds a new coefficient to the individuals. Parameters: double from : minimum value of the coefficient double to : maximum value of the coefficient

TMVA::GeneticGenes* GetGenes( Int_t index )

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gives back the "Genes" of the population with the given index.
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Double_t GetFitness( Int_t index )

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gives back the calculated fitness of the individual with the given index
(after the evaluation of the fitness ["calculateFitness"] index==0
is the best individual.
```

void ClearResults()

delete the results of the last calculation of the fitnesses of the population. (to prepare a new Generation)

TMVA::GeneticGenes* GetGenes()

get the Genes of where an internal pointer is pointing to in the population

Bool_t SetFitness( TMVA::GeneticGenes *g, Double_t fitness, Bool_t add )

set the fitness of "g" to the value "fitness". add==true indicates, that this individual is created newly in this generation if add==false, this is a reavaluation of the fitness of the individual.

void GiveHint( vector< Double_t > hint, Double_t fitness )

if there is some good configuration of coefficients one might give this Hint to the genetic algorithm. Parameters: vector< double > hint : is the collection of coefficients double fitness : is the fitness this collection has got

void Print( Int_t untilIndex )

make a little printout of the individuals up to index "untilIndex" this means, .. write out the best "untilIndex" individuals.

void Print( ostream & out, Int_t untilIndex )

make a little printout to the stream "out" of the individuals up to index "untilIndex" this means, .. write out the best "untilIndex" individuals.

TH1F* VariableDistribution( Int_t varNumber, Int_t bins, Int_t min, Int_t max )

give back a histogram with the distribution of the coefficients parameters: int bins : number of bins of the histogram int min : histogram minimum int max : maximum value of the histogram

vector<Double_t> VariableDistribution( Int_t varNumber )

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gives back all the values of coefficient "varNumber" of the current generation
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