[] = simple_verbose(k,AllStat,verbose_params,auxedaparams)
simple_verbose: Prints a number of statistics (depending on verbose_params) about generation k of the EDA
INPUTS
k: Current generation
AllStat: Array containing the statistics of the populations.
It is updated by the method
AllStat{k,1}= matrix of 5 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} = time_operations(k,:) Matrix with the time in seconds spent at the main
EDA steps at each generation, each of the 6 column stores the times for the
following steps {sampling,evaluation,replacement,selection,learning and
total (which consider the time by the previous
5 and other EDA operations)}
AllStat{k,6} = Vector with the number of evaluations up to each generation
verbose__params{1}: find_bestinds_method: Name of the procedure for selecting the best individuals
from a population (by default is 'fitness_ordering')
auxedaparams: Contains the description of all EDA methods and
parameters
OUTPUTS
AllStat: Array containing the statistics of the population
Last version 8/26/2008. Roberto Santana (roberto.santana@ehu.es)0001 function[] = simple_verbose(k,AllStat,verbose_params,auxedaparams) 0002 % [] = simple_verbose(k,AllStat,verbose_params,auxedaparams) 0003 % simple_verbose: Prints a number of statistics (depending on verbose_params) about generation k of the EDA 0004 % INPUTS 0005 % k: Current generation 0006 % AllStat: Array containing the statistics of the populations. 0007 % It is updated by the method 0008 % AllStat{k,1}= matrix of 5 rows and number_objectives 0009 % columns. Each row shows information about 0010 % max,mean,median,min, and variance values of the 0011 % corresponding objective in the current population 0012 % AllStat{k,2}= Stores the best individual 0013 % AllStat{k,3}= Number of different individuals 0014 % AllStat{k,4}= matrix of 5 rows and n 0015 % columns. Each row shows information about 0016 % max,mean,median,min, and variance values of the 0017 % corresponding variable in the current population 0018 % AllStat{k,5} = time_operations(k,:) Matrix with the time in seconds spent at the main 0019 % EDA steps at each generation, each of the 6 column stores the times for the 0020 % following steps {sampling,evaluation,replacement,selection,learning and 0021 % total (which consider the time by the previous 0022 % 5 and other EDA operations)} 0023 % AllStat{k,6} = Vector with the number of evaluations up to each generation 0024 % verbose__params{1}: find_bestinds_method: Name of the procedure for selecting the best individuals 0025 % from a population (by default is 'fitness_ordering') 0026 % auxedaparams: Contains the description of all EDA methods and 0027 % parameters 0028 % OUTPUTS 0029 % AllStat: Array containing the statistics of the population 0030 % 0031 % Last version 8/26/2008. Roberto Santana (roberto.santana@ehu.es) 0032 0033 0034 if(k==1) 0035 PopSize = cell2num(auxedaparams{1}(1)); 0036 n = cell2num(auxedaparams{1}(2)); 0037 F = char(cellstr(auxedaparams{1}(3))); 0038 Card = cell2num(auxedaparams{1}(4)); 0039 disp([sprintf('PopSize: %d',PopSize)]); 0040 disp([sprintf('Number of variables: %d',n)]); 0041 disp([sprintf('Function: %s',F)]); 0042 %disp([sprintf('Range of the values for the variables: ')]); 0043 %disp(Card); 0044 0045 for i=2:size(auxedaparams,1) 0046 auxstr1 = [char(cellstr(auxedaparams{i}(1)))]; 0047 auxstr2 = [char(cellstr(auxedaparams{i}(2)))]; 0048 disp([sprintf('%s: %s',auxstr1,auxstr2)]); 0049 auxparams = auxedaparams{i}(3); 0050 for j=1:size(auxparams,1) 0051 if(iscell(auxparams{j})) 0052 disp([sprintf('Params: '),auxparams{j}{:}]); 0053 else 0054 disp([sprintf('Params: '),auxparams{j}]); 0055 end 0056 end 0057 end 0058 end 0059 0060 disp([sprintf('***************** Generation %d ********************** \n',k)]); 0061 0062 disp([sprintf('Max objective values: '), sprintf('%d ',AllStat{k,1}(1,:))]); 0063 disp([sprintf('Sum of max objective values: '), sprintf('%d ',sum(AllStat{k,1}(1,:)))]); 0064 disp([sprintf('Mean objective values: '), sprintf('%d ',AllStat{k,1}(2,:))]); 0065 disp([sprintf('Sum of mean objective values: '), sprintf('%d ',sum(AllStat{k,1}(2,:)))]); 0066 disp([sprintf('Median objective values: '), sprintf('%d ',AllStat{k,1}(3,:))]); 0067 disp([sprintf('Sum of median objective values: '), sprintf('%d ',sum(AllStat{k,1}(3,:)))]); 0068 disp([sprintf('Min objective values: '), sprintf('%d ',AllStat{k,1}(4,:))]); 0069 disp([sprintf('Sum of min objective values: '), sprintf('%d ',sum(AllStat{k,1}(4,:)))]); 0070 disp([sprintf('Variance of the objective values: '), sprintf('%d ',AllStat{k,1}(5,:))]); 0071 0072 disp([sprintf('Best individual: '), sprintf('%d ',AllStat{k,2})]); 0073 disp([sprintf('Number of different individuals: '), sprintf('%d ',AllStat{k,3})]); 0074 0075 disp([sprintf('Max values of the variables: '), sprintf('%d ',AllStat{k,4}(1,:))]); 0076 disp([sprintf('Mean values of the variables: '), sprintf('%d ',AllStat{k,4}(2,:))]); 0077 disp([sprintf('Median values of the variables: '), sprintf('%d ',AllStat{k,4}(3,:))]); 0078 disp([sprintf('Min values of the variables: '), sprintf('%d ',AllStat{k,4}(4,:))]); 0079 disp([sprintf('Variance of the variables: '), sprintf('%d ',AllStat{k,4}(5,:))]); 0080 0081 disp([sprintf('number of evaluations: '), sprintf('%d ',AllStat{k,5})]); 0082 0083 disp([sprintf('Time: sampling %d, repairing %d, evaluation %d, local_opt %d, replacement %d, selection %d, learning %d, and total %d \n ',AllStat{k,6})]); 0084 0085 0086 return; 0087