def find_tsp_solution(self, fitness_function, dist_function, pop_size, gen,
m_rate, elite):
min_value = 1
max_value = self.repository.num_nodes
dim = self.repository.num_nodes + 1
ga = GA(fitness_function, min_value, max_value, dim, pop_size, elite,
m_rate)
ga.initialisation()
ga.evaluation()
overall_best = ga.best_chromosome()
progress = []
for g in range(gen):
best_chromosome = ga.best_chromosome()
progress.append(dist_function(best_chromosome.repres))
ga.next_generation_pool()
best_chromosome = ga.best_chromosome()
if best_chromosome.fitness > overall_best.fitness:
overall_best = best_chromosome
print('Best solution in generation ' + str(g) + ' is: x = ' +
str(best_chromosome.repres) + ' f(x) = ' +
str(best_chromosome.fitness))
print[i + 1 for i in overall_best.repres]
print dist_function(overall_best.repres)
self.repository.write_network("data/graph_out.txt",
overall_best.repres,
dist_function(overall_best.repres))
plt.plot(progress)
plt.ylabel('Distance')
plt.xlabel('Generation')
plt.show()
def main():
graph = read_graph_from_file(input_file)
gaParams = GAParams(populationSize=20, noOfGenerations=1000, crossoverProb=0.7, mutationProb=0.15)
problemParams = ProblemParams(network=graph, dim=len(graph), function=calculate_route_length)
ga = GA(gaParams, problemParams)
ga.initialisation()
ga.evaluation()
allBestFitnesses = []
generationsBest = []
overallBest = Chromosome(problemParams)
for generation in range(gaParams.noOfGenerations):
ga.oneGenerationSteadyState()
bestChromo = ga.bestChromosome()
print('Best solution in generation ' + str(generation) + ' f(x) = ' + str(bestChromo.fitness))
allBestFitnesses.append(bestChromo.fitness)
generationsBest.append(bestChromo)
if bestChromo.fitness < overallBest.fitness:
overallBest = bestChromo
print(overallBest.representation)
print(calculate_route_length(overallBest.representation, problemParams.network))
def ga(self):
network = self.__repo.read_file()
self.__probParam['noNodes'] = network['noNodes']
self.__probParam['mat'] = network['mat']
ga = GA(self.__param, self.__probParam)
ga.initialisation()
ga.evaluation()
g = 0
fitnessuri = []
while g < 500:
# ga.oneGenerationElitism()
ga.oneGenerationElitism2()
g += 1
best_chromo = ga.bestChromosome()
print('Best solution in generation ' + str(g) + ' is: x = ' +
str(best_chromo.repres) + '\nhas fit: ' +
str(best_chromo.fitness) + '\n')
fitnessuri.append(best_chromo.fitness)
return fitnessuri