def GenerateOffspring(parents):
offspring = []
while (len(offspring) != len(parents)):
parentA, parentB = Selection.Selection(parents)
childA, childB = Crossover.Crossover(parentA, parentB)
childA = Mutation.Mutation(childA)
childB = Mutation.Mutation(childB)
offspring.append(childA)
offspring.append(childB)
return offspring
def __init__(self, params):
self.epochs = params['epochs']
self.Crossover = Crossover(params['crossover_type'],
params['crossover_prob'])
self.Population = Population(booth_function, params['population_size'],
2, -10, 10,
params['population_precision'])
self.Mutation = Mutation(params['mutation_type'],
params['mutation_prob'])
self.Inversion = Inversion(params['inversion_type'],
params['inversion_prob'])
self.Selection = Selection(params['selection_type'],
params['selection_prob'])
self.EliteStrategy = EliteStrategy(params['elite_prob'])
def compileMutations(data, type):
mutationList = MutationList()
for drug in data['viewer']['mutationsAnalysis']['drugResistance'][0][
'drugScores']:
for mutations in drug['partialScores']:
try:
x = mutations['mutations'][1]['text']
muts = []
for mutation in mutations['mutations']:
muts.append(mutation['text'])
temp = mutations['mutations'][0]['text']
mutationList.add_multimutant(
MultipleMutant(muts, mutations['score']))
except:
temp = mutations['mutations'][0]['text']
if temp not in mutationList.get_names():
newMutation = Mutation(temp, type, mutations['score'])
mutationList.add_mutation(newMutation)
else:
mutationList.add_score(temp, mutations['score'])
mutationList.fill_blanks()
mutationList.get_averages()
return mutationList
mutation=mutation,
sizeOfPopulation=10,
genPopulation=populationGen,
numberChromosome=NN, epoche=100,
data=data)
ans = bga.fit()
print(ans)"""
data = GenMtrx_NN() # инициализация условий задачи (считывание матрицы расстояний)
NN = len(data) # считываем количество городов
fitness = Fitness.Fitness('fitness_population_NN')
crossover = Crossover.Crossover('crossover_NN')
# Можно выбрать: inbreeding_NN,
# outbreeding_NN
# panmixia_NN
# tournament_selection_NN
# roulette_selection_NN
selection = Selection.Selection('inbreeding_NN')
mutation = Mutation.Mutation('insertion_deleting_mutation_NN')
populationGen = Population.Population('elite_selection')
bga = BasicGeneticAlgorithm.BasicGeneticAlgorithm(generator=genPopulation_NN,
fitness=fitness,
crossover=crossover,
selection=selection,
mutation=mutation,
sizeOfPopulation=100,
genPopulation=populationGen,
numberChromosome=100, epoche=100,
data=data)
ans = bga.fit()
print(ans)
# # print("COMBINED IS")
# # print(combined)
# if (combined == target):
# print("Its Done!")
# sys.exit()
f = Fitness.Fitness(population)
fitness = f.getFitness()
s = Selection.Selection(fitness)
firstChoice, secondChoice, maximum = s.selectFromPop()
c = Crossover.Crossover(population, firstChoice, secondChoice)
population = c.crossover()
m = Mutation.Mutation(population, 10)
population = m.mutate()
if (catch == 1):
print(
"--------------------------------------------------------------------"
)
print("Program is finished!")
print(
"--------------------------------------------------------------------"
)
print("Final fitness: ")
print(fitness)
print(
"--------------------------------------------------------------------"