I am working shakespeare monkey experiment using genetic algorithm in python . I understood the core concept of this experiment, where an entity randomly types letter, which should finally converege with the selected target text. But when put into code, its not working, Please review the code and correct it if possible. Thank you.
# creating the shakespeare monkey experiement one last time to test my understanging about genetic algorithm
import random
text = 'Andromeda'
population_size = 501
mutation_rate = 0.02
class DNA :
genes = []
fitness_val = 0
def __init__(self,length):
for i in range(length):
self.genes.append(chr(random.randint(0,150)))
def fitness(self,target):
score = 0
self.target = target
self.fitness_val = 0
for i in range(len(target)):
if self.genes[i] == self.target[i]:
score += 1
self.fitness_val = score / len(self.target)
def crossover(self,parent):
child = DNA(0)
midpoint = random.randint(0, len(text)-1 )
child[0:midpoint] = self.genes[0:midpoint]
child[midpoint:] = parent.genes[midpoint:]
return child
def mutate(self,mutationRate):
self.mutationRate = mutationRate
for i in range(0,len(self.genes)):
self.child.genes[i] = chr(random.randint(0,150))
def get_phrase(self):
return ''.jon(self.genes)
# creation of framework is done, now its the implementatin of this framework
class Population:
population = []
matingpool = []
def __init__(self,target,population_size,mutation_rate):
self.target = target
self.mutation_rate = mutation_rate
self.population_size = population_size
for i in range(population_size):
self.population.append(DNA(len(self.target)))
self.calculateFitness()
def calculateFitness(self):
for individual in self.population:
individual.fitness(self.target)
def selection(self):
self.matingpool.clear()
for individual in self.population:
n = int(individual.fitness_val * 100)
for i in range(n):
self.matingpool.append(i)
def generation(self):
# parents = random.choices(self.matingpool,k=2)
# child = parents[0].crossover(parents[1])
# child.mutate(mutation_rate)
# for i in range(len(self.population)):
# #s
# self.population[i] = child
for i in range(len(self.population)):
parents = random.choices(self.matingpool,k = 2)
#print(parents)
child = parents[0].crossover(parents[1])
#print(child)
child.mutate(mutation_rate)
self.population[i] = child
return child
def select_best(self):
threshold = 0
index = 0
for i in range(len(self.population)):
if self.population[i].fit > threshold:
threshold = self.population[i].fit
index = i
pop = Population(text,population_size,mutation_rate)
converged = True
while converged:
pop.selection()
pop.generation()
pop.calculateFitness()
if pop.get_phrase() == text:
converged = False
enter image description here This is the result I am getting. It says there is an index error.
