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📄 restrictedtournamentreplacement.m

📁 This matlab code on reed solomon and BCH encoding and different decoding algorithms. Also errors and
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% File: restrictedTournamentReplacement.m
%
% Description: Perform restricted tournament replacement. For every
% offspring select the closest parent from among w randomly chosen parents.
% If the offspring is better than the closest parent then it replaces the
% parent.
%
% @param population is the parent population
%
% @param fitness is the fitness of parent individuals
%
% @param newPopulation is the offspring population
%
% @param newFitness is the fitness of offspring individuals
%
% @param windowSize is the fitness of offspring individuals
%
% @return bestPopulation is the top n individuals from the combined
% population of parent and offspring individuals.
%
% @return bestFitness is the fitness of the top n individuals from the
% combined population of parent and offspring individuals.
%
% Author: Kumara Sastry
%
% Date: March 2007
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

function [population, fitness] = restrictedTournamentReplacement(population, fitness, newPopulation, newFitness, windowSize)

% Get the number of offspring individuals
n = size(newPopulation,1);

% For each offspring individual
for i = 1:n,
    
    % Randomly select windowSize individuals from the parent population
    shuffleArray = randperm(n);
    chosenParentsIndex = shuffleArray(1:windowSize);
    
    %Compute the hamming distance of the offspring with the randomly
    %selected parents
    hammingDistance = sum(abs(population(chosenParentsIndex,:)-repmat(newPopulation(i,:),windowSize,1)),2);

    % Find the closest parent
    [closestDistance, closestDistanceIndex] = min(hammingDistance);
    closestParent = chosenParentsIndex(closestDistanceIndex);
    
    % If the offspring is better than the closest parent than replace the
    % parent with the offspring.
    if(newFitness(i) > fitness(closestParent))
        population(closestParent,:) = newPopulation(i,:);
        fitness(closestParent) = newFitness(i);
    end
end

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