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 runGA():
myWorld = World([
[0, 0, 0, 0, 1, 0, 1, 3, 1],
[1, 0, 1, 1, 1, 0, 2, 3, 1],
[1, 0, 0, 1, 3, 3, 3, 3, 1],
[3, 3, 3, 1, 3, 1, 1, 0, 1],
[3, 1, 3, 3, 3, 1, 1, 0, 0],
[3, 3, 1, 1, 1, 1, 0, 1, 1],
[1, 3, 0, 1, 3, 3, 3, 3, 3],
[0, 3, 1, 1, 3, 1, 0, 1, 3],
[1, 3, 3, 3, 3, 1, 1, 1, 4],
])
ga = GA(20, 0.05, 0.9, 2, 10)
population = ga.initPopulation(128)
ga.evalPopulation(population, myWorld)
# Keep track of current generation
generation = 1
while ga.isTerminationConditionMet(generation, maxGenerations) == False:
# Print fittest individual from population
fittest = population.getFittest(0)
print("G" + str(generation) + " Best solution (" +
str(fittest.getFitness()) + "): " + fittest.toString())
# Apply crossover
population = ga.crossoverPopulation(population)
# Apply mutation
population = ga.mutatePopulation(population)
# Evaluate population
ga.evalPopulation(population, myWorld)
# Increment the current generation
generation += 1
print("Stopped after " + str(maxGenerations) + " generations.")
fittest = population.getFittest(0)
print("Best solution (" + str(fittest.getFitness()) + "): " +
fittest.toString())
