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 main():
net = readFile("date2.txt")
# print(net['mat'])
dim = net['nrCities']
problParams = {'dim': dim}
gaParams = {'dimPop': 100, 'nrGen': 200}
ga = GA(net['mat'], problParams, gaParams)
ga.initPopulation()
ga.evaluateFitness()
maxFitness = -1
bestRepres = []
filename = "hardE_out.txt"
f = open(filename, "w")
for gen in range(gaParams['nrGen']):
bestChrom = ga.bestChromosome()
bestSolution = ga.bestChromosome().repres
bestSolutionFitness = ga.bestChromosome().fitness
if bestSolutionFitness > maxFitness:
maxFitness = bestSolutionFitness
bestRepres = bestSolution
f.write("best chromosome in generation " + str(gen) + " is " +
str(bestSolution) + " with distance = " +
str(ga.getDistance(bestChrom)) + " with fitness = " +
str(bestSolutionFitness) + "\n")
# ga.oneGeneration()
ga.oneGenerationElitism()
bestChromo = ga.bestChromosome()
repres = bestChromo.repres
repres.append(bestChromo.repres[0])
# for i in range(len(repres)):
# repres[i] += 1
f.write("best solution over all: " + str(repres) + " with distance = " +
str(ga.getDistance(bestChromo)) + " and fitness = " +
str(bestChromo.fitness) + "\n")
f.close()
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
