simple_pop_statistics

 [AllStat] = simple_pop_statistics(k,Pop,FunVal,time_operations,number_evaluations,AllStat,statistics_params)
 simple_pop_statistics:  Computes relevant statistics about EDA in each
                         generation and stores it in AllStat




 INPUTS 
 k:                   Current generation
 Pop:                 Current population
 FunVal:              A matrix of function evaluations, one vector of m objectives for each individual
 time_operations(1:k,1:6):  Matrix with the time in seconds spent at the main
                      EDA steps, each of the 8 column stores the times for the
                      following steps {sampling, repairing, evaluation, local optimization, replacement, selection,learning and
                      total (which consider the time by the previous 7 and
                      other EDA operations)
                         
 number_evaluations(1:k):  Vector with the number of evaluations in each generation
 AllStat:             Array containing the statistics of the populations.
                      It is updated by the method
                      AllStat{k,1}= matrix of 7 rows and number_objectives
                      columns. Each row shows information about
                      max,mean,median,min, and variance values of the
                      corresponding objective in the current population
                      AllStat{k,2}= Stores the best individual
                      AllStat{k,3}= Number of different individuals
                      AllStat{k,4}= matrix of 5 rows and n
                      columns. Each row shows information about
                      max,mean,median,min, and variance values of the
                      corresponding variable in the current population
                      AllStat{k,5} = number_evaluations(k,:) (see inputs)
                      AllStat{k,6} = time_operations(k,:) (see inputs)
 statistics_params(1): find_bestinds_method: Name of the procedure for selecting the  best individuals 
                                                 from a population (by default is 'fitness_ordering')
 OUTPUTS
 AllStat: Array containing the statistics of the population

 Last version 8/26/2008. Roberto Santana (roberto.santana@ehu.es)

0001 function[AllStat] = simple_pop_statistics(k,Pop,FunVal,time_operations,number_evaluations,AllStat,statistics_params)
0002 % [AllStat] = simple_pop_statistics(k,Pop,FunVal,time_operations,number_evaluations,AllStat,statistics_params)
0003 % simple_pop_statistics:  Computes relevant statistics about EDA in each
0004 %                         generation and stores it in AllStat
0005 %
0006 %
0007 %
0008 %
0009 % INPUTS
0010 % k:                   Current generation
0011 % Pop:                 Current population
0012 % FunVal:              A matrix of function evaluations, one vector of m objectives for each individual
0013 % time_operations(1:k,1:6):  Matrix with the time in seconds spent at the main
0014 %                      EDA steps, each of the 8 column stores the times for the
0015 %                      following steps {sampling, repairing, evaluation, local optimization, replacement, selection,learning and
0016 %                      total (which consider the time by the previous 7 and
0017 %                      other EDA operations)
0018 %
0019 % number_evaluations(1:k):  Vector with the number of evaluations in each generation
0020 % AllStat:             Array containing the statistics of the populations.
0021 %                      It is updated by the method
0022 %                      AllStat{k,1}= matrix of 7 rows and number_objectives
0023 %                      columns. Each row shows information about
0024 %                      max,mean,median,min, and variance values of the
0025 %                      corresponding objective in the current population
0026 %                      AllStat{k,2}= Stores the best individual
0027 %                      AllStat{k,3}= Number of different individuals
0028 %                      AllStat{k,4}= matrix of 5 rows and n
0029 %                      columns. Each row shows information about
0030 %                      max,mean,median,min, and variance values of the
0031 %                      corresponding variable in the current population
0032 %                      AllStat{k,5} = number_evaluations(k,:) (see inputs)
0033 %                      AllStat{k,6} = time_operations(k,:) (see inputs)
0034 % statistics_params(1): find_bestinds_method: Name of the procedure for selecting the  best individuals
0035 %                                                 from a population (by default is 'fitness_ordering')
0036 % OUTPUTS
0037 % AllStat: Array containing the statistics of the population
0038 %
0039 % Last version 8/26/2008. Roberto Santana (roberto.santana@ehu.es)
0040 
0041 find_bestinds_method = char(cellstr(statistics_params{1}(1)));
0042 
0043          AllStat{k,1} =  [max(FunVal);  % Statistics of the fitness objectives
0044                           mean(FunVal);
0045                           median(FunVal);
0046                           min(FunVal);
0047                           var(FunVal)];          
0048               
0049         [Ind]  = eval([find_bestinds_method,'(Pop,FunVal)']);  % The  best individual is found
0050         
0051         AllStat{k,2} = Pop(Ind(1),:);                                % Best individual
0052         AllStat{k,3} = size(unique(Pop,'rows'),1);                % Number of different individuals
0053        
0054         
0055         AllStat{k,4} =   [max(Pop);              % Statistics of the population
0056                           mean(Pop);
0057                           median(Pop);
0058                           min(Pop);
0059                           var(Pop)];
0060       
0061        AllStat{k,5} = number_evaluations(k);                    
0062        AllStat{k,6} = time_operations(k,:);       
0063 
0064         
0065             
0066 return;        
0067