TMVA::GeneticBase

 Constructor
 Parameters: 
     int populationSize : defines the number of "Individuals" which are created and tested 
                          within one Generation (Iteration of the Evolution)
     vector<LowHigh_t*> ranges : LowHigh_t is a pair of doubles, where the first entry 
                          is the low and the second entry 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()

 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. 

 calls calculateFitness

 the "fitnessFunction" 
 this function is designed to be overridden. 
 when overridden it should give back a (double) value which 
 tells the "fitness" (= quality) of the result. This might be 
 a resolution, a distance, ... 
 
 Parameter:
       vector< double > factors : the factors are given by the 
                     Genetic Algorithm. These are the factors that
                     have to be tested to assess its quality.

 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. 

 the fitness values of every individual is stored ..
 if the fitness has been evaluated for many events, all the results are 
 internally stored. 

 this function allows to loop through all results of all individuals. 
 it calls implicitly the function "renewFitness" 
 
 the right place to call this function would be at the end of one "Generation"
 to set the fitness of every individual new depending on all the results it obtained 
 in this generation. 

 this function has to be overridden if "doRenewFitness" is called
 Parameters: 
         vector< double > factors : in this vector the factors of a specific individual
                      are given. 
         vector< double > results : in this vector the results obtained by the given
                     coefficients are given. 
 
 out of this information (the quality of the results) a new? value for the quality 
 (fitness) of the set of factors has to be given back.

 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. 

 nothing so far...