def main():
network = Utils.read_berlin("exemplu.txt")
gaParam = {
'popSize': 200,
'noGen': 2000,
'pc': 0.8,
'pm': 0.1
}
problParam = {
'min': 1,
'max': network['noNodes'],
'fitness_function': Utils.modularity,
'noNodes': network['noNodes'],
'matrix': network['matrix']
}
ga = GA(gaParam, problParam)
for g in range(gaParam['noGen']):
ga.generate_newGeneration()
bestChromo = ga.best_chromosome()
worstChromo = ga.worst_chromosome()
print('Best solution in generation ' + str(g) + ' is: x = ' + str(bestChromo.repres) + ' f(x) = ' + str(
bestChromo.fitness))
print('Worst solution in generation ' + str(g) + ' is: x = ' + str(worstChromo.repres) + ' f(x) = ' + str(
worstChromo.fitness))
print()
def main():
network = fileUtils.read_from_directory(4)
gaParam = {'size': 100, 'generations': 100}
problParam = {
'noNodes': network['noNodes'],
'matrix': network['matrix'],
'function': fitness
}
ga = GA(gaParam, problParam)
ga.initialization()
ga.evaluation()
for generation in range(gaParam['generations']):
# ga.one_generation_steady_state()
# ga.one_generation_elitism()
ga.one_generation_elitism_improved()
best = ga.best_chromosome()
print('Solutia optima in generatia ' + str(generation + 1) + ' este ' +
str(best.repres) + 'fitness = ' + str(best.fitness))
best = ga.best_chromosome()
print('\nSolutia optima : ' + str(best.repres) +
'\n fitness = ' + str(best.fitness))
def main():
file_name = "net.in"
net = read_net(file_name)
problParam = {
'graph': net['graph'],
'function': fitness_funct,
'size': net['no_nodes']
}
param = {'popSize': 200, 'noGen': 5000, 'mut_rate': 50}
ga = GA(param, problParam)
ga.initialisation()
ga.evaluation()
g = 0
graph = net['graph']
while g < param['noGen']:
g += 1
ga.one_generation()
chromo = ga.best_chromosome()
print(chromo.fitness)
