def GA_Version_1(version_path, Runs, generations, parent_pop_init, mating_pop,
mutationRate, mutationFunc, passes, instance):
minFitnessValsOfAllRuns = []
averageFitnessValsOfAllRuns = []
for r in range(0, Runs):
img_path = version_path + "GA instance " + str(instance) + "/Images/"
initial_population_path = version_path + "GA instance " + str(
instance) + "/initial_population" + " for Run " + str(r +
1) + ".json"
final_population_path = version_path + "GA instance " + str(
instance) + "/final_population" + " for Run " + str(r +
1) + ".json"
data_path = version_path + "GA instance " + str(instance) + "/"
random_seed = 100 * (r + 1)
np.random.seed(random_seed)
LB = 0
UB = 1
initial_image = cv2.imread("binaryhand.png", 0)
initialize_population(random_seed,
initial_image,
parent_pop_init,
bonus=False)
with open("CellularAutomata.json", 'r') as f:
population = json.loads(f.read())
start_image = np.asarray(population[0])
final_image = np.asarray(population[0])
goal_image = np.asarray(population[1])
save_image(img_path, initial_image, "initial_image", r)
save_image(img_path, start_image, "start_image", r)
save_image(img_path, goal_image, "goal_image", r)
goal_image_binary = grayToBinary(goal_image)
save_image(img_path, goal_image_binary, "goal_image_binary", r)
population_used = copy.deepcopy(population[2:])
save_data(initial_population_path, population_used)
s = 0
minFitnessValsPerRun = []
averageFitnessValsPerRun = []
diversity_initial = calculateDiversity(population_used)
#print("Initial diversity= "+str(diversity_initial)+" for Run: "+str(r+1))
for epoch in range(0, generations):
parent_pop_size = len(population_used)
parentPopfitnessVals = evaluatePopulationFitness(
population_used, parent_pop_size, passes, final_image,
goal_image_binary)
[minfitness,
bestSolution] = solutionTaggerSorter(population_used,
parentPopfitnessVals,
parent_pop_size, 1, False)
minfitness = minfitness[0]
averagefitness = stat.mean(parentPopfitnessVals)
matingPool = population_used
muArgs = {
"matingPool": matingPool,
"mutationRate": mutationRate,
"LB": LB,
"UB": UB
}
mutationStrategy, args = mutationStrategySelector(
mutationFunc, muArgs)
lamb = len(matingPool)
offsprings = mutationFunction(mutationStrategy, args)
offspringFitnessVals = evaluatePopulationFitness(
offsprings, lamb, passes, final_image, goal_image_binary)
newGenPop = crowding(population_used, offsprings,
parentPopfitnessVals, offspringFitnessVals,
parent_pop_size)
if epoch == 0:
initFitness = minfitness
population_used = newGenPop
averageFitnessValsPerRun.append(averagefitness)
minFitnessValsPerRun.append(minfitness)
diversity_current = calculateDiversity(newGenPop)
# print("diversity of current generation= "+str(diversity_current)+" for Run: "+str(r+1))
# print("epoch="+str(epoch)+" for Run: "+str(r+1))
# print("minfitness="+str(minfitness)+" for Run: "+str(r+1))
# print("\n")
# print("initminfitness="+str(initFitness)+" for Run: "+str(r+1))
# print("finalminfitness="+str(minfitness)+" for Run: "+str(r+1))
# print("\n")
print(" Run: " + str(r + 1))
save_values(data_path + "data" + " for Run " + str(r + 1) + ".json", [
diversity_initial, diversity_current, initFitness, minfitness,
bestSolution
], [
"Initial diversity", "final diversity", "Initial minimum fitness",
"final minimum fitness", "Best Solution"
])
save_image(img_path, getFinalImage(start_image, bestSolution, passes),
"final_image_binary", r)
save_data(final_population_path, population_used)
minFitnessValsOfAllRuns.append(minFitnessValsPerRun)
averageFitnessValsOfAllRuns.append(averageFitnessValsPerRun)
stats = progressOfEvolution(Runs, generations, minFitnessValsOfAllRuns,
averageFitnessValsOfAllRuns)
return stats
def GA_Version_1(Runs, generations, parent_pop_init, mating_pop, mutationRate,
crossoverRate, mutationFunc, crossoverFunc, passes):
print("GA version 1 begins here")
minFitnessValsOfAllRuns = []
averageFitnessValsOfAllRuns = []
for r in range(0, Runs):
random_seed = 100 * (r + 1)
np.random.seed(random_seed)
LB = 0
UB = 1
initial_image = cv2.imread("binaryhand.png", 0)
initialize_population(initial_image, parent_pop_init, bonus=False)
with open("CellularAutomata.json", 'r') as f:
population = json.loads(f.read())
start_image = np.asarray(population[0])
final_image = np.asarray(population[0])
goal_image = np.asarray(population[1])
visualize_image(initial_image)
visualize_image(start_image)
visualize_image(goal_image)
goal_image_binary = grayToBinary(goal_image)
population_used = copy.deepcopy(population[2:])
s = 0
minFitnessValsPerRun = []
averageFitnessValsPerRun = []
diversity_initial = calculateDiversity(population_used)
print("Initial diversity= " + str(diversity_initial) + " for Run: " +
str(r + 1))
for epoch in range(0, generations):
parent_pop_size = len(population_used)
parentPopfitnessVals = evaluatePopulationFitness(
population_used, parent_pop_size, passes, final_image,
goal_image_binary)
[minfitness,
bestSolution] = solutionTaggerSorter(population_used,
parentPopfitnessVals,
parent_pop_size, 1, False)
minfitness = minfitness[0]
averagefitness = stat.mean(parentPopfitnessVals)
matingPool = fitnessProportionateSelectionWithoutScaling(
population_used, parentPopfitnessVals, s, parent_pop_size,
mating_pop)
muArgs = {
"matingPool": matingPool,
"mutationRate": mutationRate,
"LB": LB,
"UB": UB
}
crArgs = {
"matingPool": matingPool,
"mating_pop": mating_pop,
"LB": LB,
"UB": UB,
"crossoverRate": crossoverRate
}
mutationStrategy, args = mutationStrategySelector(
mutationFunc, muArgs)
crossoverStrategy, args1 = crossoverStrategySelector(
crossoverFunc, crArgs)
crossoverFunction(crossoverStrategy, args1)
lamb = len(matingPool)
offsprings = mutationFunction(mutationStrategy, args)
offspringFitnessVals = evaluatePopulationFitness(
offsprings, lamb, passes, final_image, goal_image_binary)
newGenPop = crowding(population_used, offsprings,
parentPopfitnessVals, offspringFitnessVals,
parent_pop_size)
if epoch == 0:
initFitness = minfitness
population_used = newGenPop
averageFitnessValsPerRun.append(averagefitness)
minFitnessValsPerRun.append(minfitness)
diversity_current = calculateDiversity(newGenPop)
print("diversity of current generation= " +
str(diversity_current) + " for Run: " + str(r + 1))
print("epoch=" + str(epoch) + " for Run: " + str(r + 1))
print("minfitness=" + str(minfitness) + " for Run: " + str(r + 1))
print("initminfitness=" + str(initFitness) + " for Run: " + str(r + 1))
print("finalminfitness=" + str(minfitness) + " for Run: " + str(r + 1))
visualize_image(getFinalImage(start_image, bestSolution, passes))
minFitnessValsOfAllRuns.append(minFitnessValsPerRun)
averageFitnessValsOfAllRuns.append(averageFitnessValsPerRun)
stats = progressOfEvolution(Runs, generations, minFitnessValsOfAllRuns,
averageFitnessValsOfAllRuns)
print("GA version 1 ends here")
return stats
initialize_population(initial_image, 1, bonus=True)
with open("CellularAutomata.json", 'r') as f:
population = json.loads(f.read())
start_image = np.asarray(population[0])
goal_image = np.asarray(population[1])
start_image_binary = grayToBinary(start_image)
plt.imshow(start_image_binary, cmap="gray")
plt.show()
goal_image_binary = grayToBinary(goal_image)
name = imgs[i][:-4]
final_image = getFinalImage(start_image, ruleTable, 1)
psnr_GA[name] = psnr(goal_image, final_image, data_range=255)
msssim_GA[name] = msssim(goal_image, final_image, MAX=255)
fitness_GA[name] = evaluatePopulationFitness(ruleTable, 1, 1, start_image,
goal_image_binary)
print(psnr_GA)
print(msssim_GA)
print(fitness_GA)
print('myimage')
save_image(img_path, start_image, "start_" + name)
save_image(img_path, goal_image, "goal_" + name)
save_image(img_path, final_image, "final_" + name)
#with open('',)