userdefinedparameters.m

This matlab code on reed solomon and BCH encoding and different decoding algorithms. Also errors and

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% File: userDefinedParameters.m
%
% Description: Contains all the user-defined eCGA parameters
%
% @return parameters is a cell array with parameter values such as number
% of variables, population size, alphabet cardinalities, selection method,
% parameters for the selection method, replacement method, parameters for
% the replacement method (if any), termination critiera and parameters for
% the termination criteria.
%
% Author: Kumara Sastry
%
% Date: March 2007
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


function parameters = userDefinedParameters()

% Number of variables, Change it appropriately for your problem
numVariables = 24;

% Population size. Change it based on the number of decision variables,
% sub-structural complexity, signal-to-noise ratio, error tolerance, and
% substructural scaling.
populationSize = 400;

% Alphabet cardinalities. Change it appropriately for your problem
ranges(1:numVariables) = 2;

% Selection method: Tournament and trucation selection. Available options
% are 1. TOURNAMENT, and 2. TRUNCATION. Default is tournament selection
selectionMethod = 'TOURNAMENT';
if(strcmpi(selectionMethod, 'TRUNCATION'))
    % Truncation parameter: Should be in the range (0, 1)
    selectionParameter = 0.5;
    
    % Makes sure that the parameter is within the acceptable range.
    if(selectionParameter < 0.0 || selectionParameter > 1.0)
        selectionParameter = 0.5;
    end
else
    % Tournament size
    selectionParameter = 8;
    % Make sure that the population size is a multiple of tournament size
    populationSize = populationSize + mod(populationSize,selectionParameter);
end
selection = {selectionMethod, selectionParameter};

% Replacement stragegy: Default is generational replacement. Available
% options are: 1. RTR, 2. ELITIST, and 3. DEFAULT
replacementMethod = 'RTR';
if(strcmpi(replacementMethod,'RTR'))
    replacementParameter = min(numVariables, populationSize/10);
end
replacement = {replacementMethod, replacementParameter};

% Convergence critieria parameters
% Maximum number of generations
maxGen = 10; 

% Maximum fitness variance tolerated
maxFitVar = 1.0e-5;

%Maximum fitness value desired (e.g., fitness of the optimal solution)
maxFit = numVariables/4; 

%Maximum number unique individuals in the converged population
maxPopVar = 1;  

% The current implementation can handle more than one termination criteria.
% eCGA run is terminated even if one of the criteria is satisfied.
convergenceCriteria = {'MAXGEN', 'MAXFIT', 'FITVAR',  'POPVAR'};
convergenceParameter = {maxGen, maxFit, maxFitVar, maxPopVar};
convergence = {convergenceCriteria, convergenceParameter};

parameterValues = {numVariables, populationSize, ranges, selection, replacement, convergence};
parameterLabels = {'NumVariables', 'PopulationSize', 'Cardinalities', 'Selection', 'Replacement', 'Convergence'};
parameters = cell2struct(parameterValues, parameterLabels, 2);