def init(su):
su.population = []
su.varLength = []
#Setting the variables boundaries
for domain in su.varDomain:
if(su.geneType == "Binary string"):
su.varLength.append(32)
else:
su.varLength.append(1)
i = 0 #Counter for population individuals
counter = 0 #Validity counter
'''Initial population starts the proccess with random values'''
while True:
#Checking if population number was reached
if(i == su.populationSize) :
break
if(counter == 2 * su.populationSize): #If the counter doubles the population size, tere is something wrong with the function or domain so the evolution stops
return False
su.population.append([])
for domain in su.varDomain:
if(su.geneType == "Binary string"):
floatVar = round(rd.uniform(domain[0], domain[1]), 3)
binaryVar = float2bin(floatVar)
su.population[i].extend(binaryVar)
elif(su.geneType == "Integer string"):
intVar = rd.randint(domain[0], domain[1])
su.population[i].append(intVar)
else:
floatVar = rd.uniform(domain[0], domain[1])
su.population[i].append(round(floatVar, 3))
su.population[i].append(None) #Appending fitness value
fit.getFitness(su.population[i], su) #Calculating fitness
#If the fitness couldn't be calculated, the individual is eliminated and the validity counter is incremented
if(su.population[i][-1] == None):
del su.population[i]
counter += 1
else:
counter = 0
i += 1
op.sort(su) #Sorting the individuals according to fitnessHistory
su.currentGeneration = 1
return True #All individuals were created and evaluated
def nonUniformMutation(chrom, su):
i = 0
aux = chrom.copy()
for domain in su.varDomain:
rand = rd.random()
if (rand <= su.mutationRate):
if su.geneType == "Float string":
if (rd.random() <= .5):
aux[i] += delta(su.currentGeneration, su.maxGenerations,
domain[1] - aux[i])
else:
aux[i] -= delta(su.currentGeneration, su.maxGenerations,
aux[i] - domain[0])
else:
if (rd.random() <= .5):
aux[i] += int(
delta(su.currentGeneration, su.maxGenerations,
domain[1] - aux[i]))
else:
aux[i] -= int(
delta(su.currentGeneration, su.maxGenerations,
aux[i] - domain[0]))
i += 1
aux = fit.getFitness(aux, su)
if aux[0]:
chrom = aux
return chrom
def flipMutation(chrom, su):
aux = chrom.copy()
for i in range(len(chrom) - 1):
rand = rd.random()
if (rand <= su.mutationRate):
aux[i] = '0' if aux[i] == '1' else '1' #Bit inversion
aux = fit.getFitness(aux, su)
if aux[0]:
chrom = aux
return chrom
def geneticAlgo():
func, ranges = fitnessfunction()
strings = []
k = 10
for i in range(k):
strings.append(generatestring(ranges))
m = 10000000000000000
state = strings[0]
for _ in range(1000):
strings = selection(strings, func)
for i in range(0, k, 2):
strings[i], strings[i + 1] = crossover(strings[i], strings[i + 1])
for i in range(k):
strings[i] = mutation(strings[i], ranges)
strings.sort(key=lambda x: getFitness(func, x))
x = getFitness(func, strings[0])
if x < m:
m = x
state = strings[0]
x = int(state[:len(state) // 2], 2)
y = int(state[len(state) // 2:], 2)
print("Minimum of the function found: {} at x = {} and y = {}".format(
m, x, y))
def uniformMutation(chrom, su):
i = 0
aux = chrom.copy()
for domain in su.varDomain:
rand = rd.random()
if (rand <= su.mutationRate):
if su.geneType == "Float string":
aux[i] = rd.uniform(domain[0], domain[1])
else:
aux[i] = rd.randint(domain[0], domain[1])
i += 1
aux = fit.getFitness(aux, su)
if aux[0]:
chrom = aux
return chrom
def selection(strings: list, f):
a = []
for i in strings:
a.append([i, getFitness(f, i)])
a.sort(key=lambda x: x[1])
b = []
for _ in range(len(strings) // 2):
x = random.randint(0, len(a) - 1)
y = random.randint(0, len(a) - 1)
if a[x][1] < a[y][1]:
b.append(a[x][0])
else:
b.append(a[y][0])
last = b[-1]
while last == a[x][0]:
x = random.randint(0, len(a) - 1)
while last == a[y][0]:
y = random.randint(0, len(a) - 1)
if a[x][1] < a[y][1]:
b.append(a[x][0])
else:
b.append(a[y][0])
return b