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SGALAB 1003 Beta 5.0.0.8( Matrix Varaible Inputs )

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SGALAB 1003 Beta 5.0.0.8( Matrix Varaible Inputs )

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15 Sep 2004 (Updated )

Genetic Algorithms Toolbox for Multi-Objective Problems with Fuzzy Logic Controller Applications

SGALAB_demo_SO_muGA.m
% /*M-FILE SCRIPT SGALAB_demo_SO_muGA MMM SGALAB */ %
% /*==================================================================================================
%  Simple Genetic Algorithm Laboratory Toolbox for Matlab 7.x
%
%  Copyright 2010 The SxLAB Family - Yi Chen - leo.chen.yi@gmail.com
% ====================================================================================================
% File description:
% We can run SGALAB by key-in "SGALAB_demo_SO_muGA" in Matlab command window
%
%Input(1):
%            options[10]:
%                      options(1)-- en-/de-coding method
%                                   'Binary' ,'b' :  binary encoding method
%                                   'Real'   ,'r' :  real number encoding method
%                                   'Literal','l' :  literal permutation encoding method
%                                   'Gray'   ,'g' :  Gray encoding method
%                                   'DNA'    ,'d' :  DNA encoding method
%                                   'Messy'  ,'m'  :  Messy encoding method
%
%                      options(2)-- selection method
%                                    'Roulettewheel', 'Roulette','Wheel','r' : Roulette wheel selection method
%                                    'Stochastic','s'                        : Stochastic selection method
%                                    'TSP_Roulettewheel','tsp_rw','tsprw'    : TSP Roulette wheel selection
%
%                      options(3)-- crossover method
%                                   'singlepoint','single'
%                                   'twopoint','two'
%                                   'N = n','n'
%                                   'random','r'
%                                   'EAX':   Travel Salesman Problem--TSP Operator
%                                            Edge Assembly Crossover( EAX ) is to do
%                                            Edge Recombination Crossover(ERX) With double edge marker,
%                                            Briefly:
%                                                    EAX = ERX + Edge_Marker
%                                    'CX' :         TSP - Cycle Crossover, CX
%                                    'OX' :         TSP - Ordered Crossover operation, OX
%                                    'PMX':         TSP - Partially Matched Crossover, PMX
%                                    'BOOLMATRIX':  TSP - Matrix Representations and Operators
%                      options(4)-- mutation  method
%                                   'singlepoint','single'
%                                   'twopoint','two'
%                                   'N = n','n'
%                                   'random','r'
%                                   'ReciprocalExchange', (Reciprocal
%                                                           Exchange. Swap two cities.)
%                                   'Displacement' , Displacement. Select a
%                                                    subtour and insert it in a random place.
%                                   'Insertion',     Select a city and
%                                                    insert it in a random place
%                                   'Inversion',      Select two points along the permutation, cut it at these points and re-insert the reversed string.
%                                                   	(1 2 | 3 4 5 6 | 7 8 9) ? (1 2 | 6 5 4 3 | 7 8 9)
%                      options(5)-- constraint_status
%                                   'with'    ,'1'--have constraint conditions
%                                   'without' ,'0'--have no constraint
%                                   conditions
%                      options(6)-- Multi-Objects option
%                                   'NON_MO'    -- Non-Multi-Objective problem
%                                   'VEGA'      -- Vector Evaluated Genetic Algorithms,J. D. Schaffer
%                                   'MOGA'      -- Multiobjective Genetic Algorithm (moGA: Fonseca and Fleming, 1993)
%                                   'NSGA'      -- Non-dominated Sorting Genetic Algorithm(NSGA Srinivas, N. and K. Deb -1994 )
%                                   'NSGAII'    -- Non-dominated Sorting Genetic Algorithm - II
%                                   'muGA'      -- Micro-genetic algorithm
%                      options(7)-- Pareto ranking method
%                                   'Goldberg','G'               -- Goldberg's method: rank = rank + 1;
%                                   'Fonceca','Fleming','MO','F' -- Fonceca and Fleming's ranking method
%                                                                   current rank
%                                                                   =  the number of its dominating individulas + 1
%                      options(8)-- Save each generation fitness data
%                                   '1', -- Save; Pareto front neet this
%                                   '0', -- Not Save,only get last results
%                      options(9)-- % mutation switcher, for Micro-GA ONLY
%                                   '1', -- invoke mutation operator
%                                   '0', -- NOT invoke mutation operator
%
%                      options(10)-- accept matrix variables
%                                   '1', -- matrix variables, same initial
%                                           range, dimension is defined in
%                                           INPUT_MATRIX_dimension.txt
%                                   '0', -- matrix variables,
%                                           with different initial values,
%                                   '-1',-- array variable, default,
%                                           no need
%                                           INPUT_MATRIX_dimension.txt
%Appendix comments:
%
%Usage:
%
% Reference:
% [1] K. Krishnakumar,(1989), "Micro-Genetic Algorithms for Stationary and  Non-Stationary Function Optimization,"
%       SPIE: Intelligent Control and Adaptive Systems, Vol. 1196,
%       Philadelphia, PA.
%
% [2]  Carlos A. Coello Coello, Gregorio Toscano Pulido, (2001), ''A
%        micro-genetic algorithm for multiobjective optimization'', Proceedings of
%        the Genetic and Evolutionary Computation Conference
%        (GECCO2001),126-140.
%===================================================================================================
%  See Also:         SGA_ENCODING ,
%                    SGA_DECODING ,
%                    SGA_SELECTION ,
%                    SGA_CROSSOVER,
%                    SGA_MUTATION,
%                    SGA_FITNESS_FUNCTION,
%                    SGA_FITNESS_EVALUATING,
%                    SGA_BENCHMARK_FUNCS,
%                    SGALAB
%
%===================================================================================================
%
%
%===================================================================================================
%Revision -
%Date        Name    Description of Change  email                 Location
%27-Jun-2003 Chen Yi Initial version        chen_yi2000@sina.com  Chongqing
%14-Jan-2005 Chen Yi update 1003            leo.chen.yi@gmail.com  Shanghai
%28-May-2007,Chen Yi add SGA_FITNESS_plot() leo.chen.yi@gmail.com  Glasgow
%07-Feb-2009 Chen Yi add fitness_data       leo.chen.yi@gmail.com  Glasgow
%15-Oct-2009 Chen Yi add n points crossover
%                    and mutation           leo.chen.yi@gmail.com  Glasgow
%02-Dec-2009 Chen Yi obsolete
%                     SGA__fitness_MO_evaluating.m
%                     SGA_FITNESS_MO_function.m, use
%                     SGA_FITNESS_function for both single and multi
%                     objective problems
%HISTORY$
%==================================================================================================*/
% SGALAB_demo_SO_muGA Begin

%% set screen


% fresh
clear all
close ('all');
warning off
% to delete old output_*.txt
!del OUTPUT_*.txt
% set working path
%cd SGALAB_Funcs
%      SGA_set_working_paths

%% begin to count time during calculating
home ;
tic % timer start >>

% data preparation

%% open data files

%%%input data files
fid1  = fopen('INPUT_min_confines.txt' , 'r' );
fid2  = fopen('INPUT_max_confines.txt' , 'r' );
fid3  = fopen('INPUT_probability_crossover.txt' , 'r' );
fid4  = fopen('INPUT_probability_mutation.txt' , 'r' );
fid5  = fopen('INPUT_population.txt' , 'r' );
fid6  = fopen('INPUT_steps.txt' , 'r' );
fid7  = fopen('INPUT_max_generation.txt' , 'r' );
fid8  = fopen('INPUT_convergence_method.txt' , 'r' );
fid9  = fopen('INPUT_max_no_change_probability_crossover_generation.txt','r');
fid10 = fopen('INPUT_deta_fitness_max.txt','r');
fid11 = fopen('INPUT_max_probability_crossover.txt','r');
fid12 = fopen('INPUT_probability_crossover_step.txt','r');
fid13 = fopen('INPUT_max_no_change_fitness_generation.txt','r');

%Micro-GA
fid14 = fopen('INPUT_muGA_population_internal_number.txt','r');    % 5, in default
fid15 = fopen('INPUT_muGA_population_replaceable_number.txt','r'); % 2, in default
fid16 = fopen('INPUT_muGA_exchangeable_number.txt','r');                         % 2, in default
fid17 = fopen('INPUT_muGA_cycle.txt','r');                         % 2, in default

fid22 = fopen('INPUT_tournament_size.txt','r');

%output data files
fid101 = fopen('OUTPUT_bestfitness.txt','w+');
fid102 = fopen('OUTPUT_maxfitness.txt','w+');
fid103 = fopen('OUTPUT_minfitness.txt','w+');
fid104 = fopen('OUTPUT_meanfitness.txt','w+');
fid105 = fopen('OUTPUT_best_result_space.txt','w+');
fid106 = fopen('OUTPUT_best_coding_space.txt','w+');
fid107 = fopen('OUTPUT_now_generation.txt','w+');
fid108 = fopen('OUTPUT_now_probability_crossover.txt','w+');

% begin to load data from file

%% read data from these files
disp('/*==================================================================================================*/')
disp('/*  Simple Genetic Algorithm Laboratory Toolbox (SGALAB) 1.0.0.3 for Matlab 7.x */ ')
disp('');
disp('    15-Oct-2009 Chen Yi update 1003     leo.chen.yi@gmail.com  Glasgow ')
disp('/*==================================================================================================*/')
disp('>>>>')
disp(' Begin to evaluate...Waiting please ...')


min_confines = fscanf( fid1 , '%g' ); min_confines = min_confines' ;

max_confines = fscanf( fid2 , '%g' ); max_confines = max_confines';

probability_crossover = fscanf( fid3 , '%g' ); probability_mutation = fscanf(fid4,'%g');

population = fscanf( fid5 , '%g' );

decimal_step = fscanf( fid6 , '%g' );

max_generation = fscanf( fid7 , '%g' );

convergence_method = fscanf( fid8 , '%g' );

max_no_change_probability_crossover_generation = fscanf( fid9 , '%g' );

deta_fitness_max = fscanf( fid10 , '%g' );

max_probability_crossover = fscanf( fid11,'%g' );

probability_crossover_step = fscanf(fid12,'%g');

max_no_change_fitness_generation = fscanf(fid13,'%g');

%Micro-GA internal population
internal_number = fscanf(fid14,'%g');

replaceable_number = fscanf(fid15,'%g');

exchangeable_number =  fscanf(fid16,'%g');

muGA_cycle       = fscanf(fid17,'%g');

decimal_step = decimal_step' ;

now_probability_crossover = probability_crossover;
%

% active tournament selection, 2009-Sep-16
tournament_size = fscanf(fid22,'%g');
      
disp('End Evaluating, List of results :')

% Step into SGALAB()

options = { 'Binary',
%              'tournament',
           'Roulettewheel', %Roulettewheel, Stochastic, tournament, truncation
    '1',% 1 points crossover, uniformly distributed
    '1',% 1 points mutation , uniformly distributed
    '0',
    'NON_MO', % will check inside SGA_entry_MO_Pareto_muGA()
    '0', % ignore
    '0', % ignore
    '1'}; % Micro-GA option:
%      1 - invoke mutation operator
%      0 - NOT invoke mutation operator

% Output
%
[ fitness_data ,...
    now_generation , ...
    now_probability_crossover,...
    best_decimal_space ,...
    best_coding_space ,...
    error_status ]= SGA__entry_SO_muGA...
    ( options,...
    min_confines ,...
    max_confines ,...
    probability_crossover ,...
    probability_mutation ,...
    population ,...
    decimal_step , ...
    max_generation ,...
    convergence_method ,...
    max_no_change_probability_crossover_generation ,...
    deta_fitness_max ,...
    max_probability_crossover ,...
    max_no_change_fitness_generation ,...
    probability_crossover_step,...
    internal_number,...
    replaceable_number,...
    muGA_cycle,...
    tournament_size);   % ONLY for tournament selection,
%                       if not, set any value to this parameter.);

% 
% [ fitness_data ,...
%     now_generation , ...
%     now_probability_crossover,...
%     best_decimal_space ,...
%     best_coding_space ,...
%     error_status ]= SGA_entry_SO_muGA...
%     ( options,...
%     min_confines ,...
%     max_confines ,...
%     probability_crossover ,...
%     probability_mutation ,...
%     population ,...
%     decimal_step , ...
%     max_generation ,...
%     convergence_method ,...
%     max_no_change_probability_crossover_generation ,...
%     deta_fitness_max ,...
%     max_probability_crossover ,...
%     max_no_change_fitness_generation ,...
%     probability_crossover_step,...
%     internal_number,...
%     replaceable_number,...
%     muGA_cycle);


if ( error_status ~= 0 )  return ;  end

%  [ maxfitness , minfitness , meanfitness , best_decimal_space , now_generation , now_probability_crossover , best_binary_space ] = SGALAB...
%  ( min_confines , max_confines , probability_crossover , probability_mutation , population , decimal_step , max_generation , convergence_method , max_no_change_probability_crossover_generation , deta_fitness_max , deta_fitness_max_min , max_probability_crossover , max_no_change_fitness_generation , probability_crossover_step );


%  maxfitness = max( fitness_value )
%  best_decimal_space = decimal_space( max_fitness_temp_position( population ) )

%write data to output files

%write data to output files
fprintf( fid101, '%f\n' , fitness_data(1) );

% fprintf( fid8 , '\n the max value of fitness function:\n' );
fprintf( fid102 , '%f\n' , fitness_data(2));

%fprintf( fid9, '\n the min value of fitness function:\n');
fprintf( fid103 , '%f\n' ,fitness_data(3));

%fprintf(fid10,'\n the mean value of fitness function:\n');
fprintf(fid104,'%f\n', fitness_data(4));

%fprintf( fid11,'\nthe best decimal space(x1 x2 x3...):\n');
fprintf( fid105,'%f\n',best_decimal_space );

fprintf( fid106 , '%f\n' , best_coding_space );

%fprintf( fid12, '\nthe generation number when end GAs:\n' );

fprintf( fid107, '%f\n' , now_generation );

fprintf( fid108, '%f\n' , now_probability_crossover );


%close files
status = fclose( 'all' );


disp(' More detail result in text files with " Output_*.txt " ' )
disp('----------------------------------------------------------------------------------------')
result_files = list_current_dir_files ('OUTPUT_*.txt')

disp('----------------------------------------------------------------------------------------')

% timer end
toc
clear all
%  SGA_remove_work_paths

% SGALAB_demo_SO_muGA End

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