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GeneticAlgorithm.m
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GeneticAlgorithm.m
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classdef GeneticAlgorithm
properties
m_mutation_rate;
m_population_size;
m_chromosone_length;
m_population;
m_structure;
m_connections;
m_children;
m_network;
end
methods
function obj = GeneticAlgorithm(mutation_rate, population_size, neurons, connections)
obj.m_mutation_rate = mutation_rate;
obj.m_population_size = population_size;
n = sum(neurons);
%node count multiply rule parameter encoding and tau minus
%count of output node + fitness
obj.m_chromosone_length = n * (1 + 10 + 5) - neurons(end) * (1 + 10 + 5) + 1;
obj.m_structure = neurons;
obj.m_connections = connections;
len = obj.m_population_size;
chromosone = obj.m_chromosone_length;
obj.m_population = zeros(len, chromosone);
obj.m_children = zeros(len, chromosone);
end
function obj = Initialization(obj, init)
for i = init : obj.m_population_size
for j = 1 : obj.m_chromosone_length
obj.m_population(i, j) = randi([0 1]);
end
end
end
function net = Decoding(obj, index, population)
binary2num([1 1], 10);
a = load('net');
net = a.net;
len = length(obj.m_structure);
struct = obj.m_structure;
l = 0;
for i = 1 : len - 1
for j = 1 : obj.m_structure(i)
if i > 1
l = i - 1;
else
l = i;
end
ind = ((i > 1)*struct(l) + j) * 16 - 16 + 1;
net.neural{i}{1}{j}.rule = population(index, ind);
net.neural{i}{1}{j}.t = binary2num(population(index, ind + 11 : ind + 16), 100);
A = binary2num(population(index, ind + 1 : ind + 10), 1000);
net.neural{i}{1}{j}.A1 = A;
net.neural{i}{1}{j}.A2 = A;
end
end
end
function obj = Fitness(obj, populate)
if populate == true
population = obj.m_population;
else
population = obj.m_children;
end
len = size(obj.m_population);
len = len(1);
xRef = 10.1;
yRef = 10.1;
for i = 1 : len
net = Decoding(obj, i, population);
for k = 1:3
[x, y, wR, wL, net] = RunSim(net, 10.1, 10.1, 5);
end
sumPath = 0;
for k = 1:length(x)
sumPath = sumPath + sqrt((x(k) - xRef)^2 + (y(k) - yRef)^2);
end
population(i, end) = 100 / sumPath;
end
if populate == true
obj.m_population = population;
else
obj.m_children = population;
end
end
function index = Selection(obj, population)
choose1 = randi([1 obj.m_population_size]);
choose2 = randi([1 obj.m_population_size]);
while choose1 == choose2
choose2 = randi([1 obj.m_population_size]);
end
if population(choose1, end) > population(choose2, end)
index = choose1;
else
index = choose2;
end
end
function obj = Crossover(obj)
len = obj.m_population_size;
for i = 1 : len
divide_point = randi([1 (sum(obj.m_structure) - obj.m_structure(end) - 1)]);
parent1 = Selection(obj, obj.m_population);
parent2 = Selection(obj, obj.m_population);
obj.m_children(i, 1 : divide_point * 16) = obj.m_population(parent1, 1 : divide_point * 16);
obj.m_children(i, divide_point * 16 + 1 : end - 1) = obj.m_population(parent2, divide_point * 16 + 1 : end - 1);
end
end
function obj = Mutation(obj)
len = obj.m_population_size;
len1 = obj.m_chromosone_length - 1;
mutation_probability = obj.m_mutation_rate;
for i = 1 : len
for j = 1 : len1
if rand([1 1]) <= mutation_probability
if obj.m_children(i, j) == 1
obj.m_children(i, j) = 0;
else
obj.m_children(i, j) = 1;
end
end
end
end
end
function index = GetBest(obj, population)
maximum = population(1, end);
len = obj.m_population_size;
index = 1;
for i = 2 : len
if population(i, end) > maximum
maximum = population(i, end);
index = i;
end
end
end
function obj = Replace(obj)
obj.m_population(1, :) = obj.m_population(GetBest(obj, obj.m_population), :);
obj.m_population(2, :) = obj.m_children(GetBest(obj, obj.m_children), :);
len = obj.m_population_size;
for i = 3 : len
parent = Selection(obj, obj.m_population);
child = Selection(obj, obj.m_children);
if obj.m_population(parent, end) > obj.m_children(child, end)
obj.m_population(i, :) = obj.m_population(parent, :);
else
obj.m_population(i, :) = obj.m_children(child, :);
end
end
end
function obj = Evolve(obj, generations)
obj = Initialization(obj, true);
obj = Fitness(obj, true);
for i = 1 : generations
obj = Crossover(obj);
obj = Mutation(obj);
obj = Fitness(obj, false);
obj = Replace(obj);
top = GetBest(obj, obj.m_population);
net = Decoding(obj, top, obj.m_population);
for k = 1:3
[x, y, wR, wL, net] = RunSim(net, 10.1, 10.1, 5);
end
obj.m_network = net;
figure();
plot(x, y)
hold all
plot(10.1, 10.1, 'r+');
grid;
if mod(i, 20) == 0
half = ceil(obj.m_population_size / 2);
obj = Initialization(obj, half);
obj = Fitness(obj, true);
end
end
end
end
end