simulated annealing code vs genetic algorithm code for minimize fitness function in matlab

main.m
% Define the fitness function
fitnessFunction = @(x) x^2; % Insert your own fitness function

% Define the initial state (solution)
initialState = rand(); % You can change the initial state as per your requirement

% Define the temperature parameters
initialTemperature = 100; % You can change the initial temperature as per your requirement
coolingRate = 0.95; % You can change the cooling rate as per your requirement
finalTemperature = 0.1; % You can change the final temperature as per your requirement

% Initialize the current state and current fitness
currentState = initialState;
currentFitness = fitnessFunction(currentState);

% Start the simulated annealing algorithm
temperature = initialTemperature;
while temperature > finalTemperature
    % Generate a random neighboring state
    newState = currentState + randn();
    
    % Calculate the fitness of the new state
    newFitness = fitnessFunction(newState);
    
    % Calculate the acceptance probability
    acceptanceProbability = exp((currentFitness - newFitness) / temperature);
    
    % Decide whether to accept the new state or not
    if newFitness < currentFitness || rand() < acceptanceProbability
        currentState = newState;
        currentFitness = newFitness;
    end
    
    % Cool down the temperature
    temperature = temperature * coolingRate;
end

% Display the final result
disp('Simulated Annealing Results:');
disp(['Best Solution: ' num2str(currentState)]);
disp(['Best Fitness: ' num2str(currentFitness)]);
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main.m
% Define the fitness function
fitnessFunction = @(x) x^2; % Insert your own fitness function

% Define the optimization problem parameters
populationSize = 50; % You can change the population size as per your requirement
numberOfGenerations = 100; % You can change the number of generations as per your requirement

% Initialize the population
population = rand(populationSize, 1); % You can change the initialization method as per your requirement

% Start the genetic algorithm
for generation = 1:numberOfGenerations
    % Calculate the fitness of each individual in the population
    fitness = fitnessFunction(population);
    
    % Perform selection, crossover, and mutation to create the next generation
    offspring = selection(population, fitness);
    offspring = crossover(offspring);
    offspring = mutation(offspring);
    
    % Replace the current population with the offspring
    population = offspring;
end

% Find the best solution in the final population
fitness = fitnessFunction(population);
[bestFitness, bestIndex] = min(fitness);
bestSolution = population(bestIndex);

% Display the final result
disp('Genetic Algorithm Results:');
disp(['Best Solution: ' num2str(bestSolution)]);
disp(['Best Fitness: ' num2str(bestFitness)]);

% Helper functions for genetic algorithm (selection, crossover, and mutation)
function offspring = selection(population, fitness)
    [~, sortedIndices] = sort(fitness);
    eliteIndex = sortedIndices(1);
    rouletteWheel = cumsum(fitness(sortedIndices) / sum(fitness(sortedIndices)));
    offspring = population(eliteIndex);
    for i = 2:length(population)
        randomValue = rand();
        selectedParent = find(rouletteWheel >= randomValue, 1);
        offspring = [offspring; population(selectedParent)];
    end
end

function offspring = crossover(population)
    offspring = zeros(size(population));
    for i = 1:length(population)
        parent1 = population(i);
        parent2 = population(randi(length(population)));
        offspring(i) = (parent1 + parent2) / 2; % You can change the crossover method as per your requirement
    end
end

function offspring = mutation(population)
    offspring = population + randn(size(population)); % You can change the mutation method as per your requirement
end
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