class TMVA::GeneticAlgorithm

 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()

 destructor; deletes fLogger

 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.

 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;

 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.

 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.

 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.

 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.