def natselect(popn, iteration):
tpopn = list()
while len(tpopn) < POPULATION_SIZE * 8:
if random.random() < .5:
child = indermediary_recomb(parentselect(popn))
mutate(child, iteration)
tpopn.append(individual(child, fitness(child)))
else:
child = discrete_recomb(parentselect(popn))
mutate(child, iteration)
tpopn.append(individual(child, fitness(child)))
return survivorselect(tpopn)
def main(A, B, C, eps, sim_steps = 100):
"""
Genetic Algorithm simulation with a given number of steps and initial values: A, B, C, eps,
required for the goal function calculation.
:return: Last population after GA simulation is finished.
"""
steps = int(sim_steps) # simulation steps
p_c = 0.15 # cross over probability # 0.15
p_m = 0.0005 # mutation probability # 0.0005
p_i = 0.0001 # inversion probability # 0.0001
pop_size = 200 # 200
### INITIALIZATION ###
pop = func.init(pop_size, 'multinomial')
best_fs = [] # goal function of the best one in populationin each step
fs = [] # summary goal function of the population in each step
for ii in range(0, steps):
##### 1 SELECTION
p_sel = func.selection(pop, A, B, C, eps)
fs.append(np.sum(p_sel))
best_fs.append(np.max(p_sel))
new_pop = func.roulette_select(pop, p_sel, pop_size)
##### 2 CROSSOVER
pop = func.crossover(new_pop, pop_size, p_c)
###### 3 MUTATION
for cc in range(0, len(pop)):
pop[cc] = func.mutate(pop[cc], p_m)
###### 4 INVERSION
probs_inv = np.random.uniform(size=len(pop))
for nn in range(0, len(probs_inv)):
if probs_inv[nn] < p_i:
pop[nn] = func.inverse(pop[nn])
# after the GA steps >= simulation steps - i.e. the result of GA
# the code below calculates results averaged over the whole population
vals = np.zeros(16)
get_av = False
if get_av:
for ii in range(0, 16):
for em in pop:
vals[ii] += em[ii]
vals /= pop_size
terminal_view(vals)
return pop, vals, np.array(best_fs), fs
elit = population[np.where(fit == fittest)]
fitness_history[iter, gen] = fittest
best_results_gen.append(fittest)
if gen > 0:
if best_results_gen[j] == best_results_gen[j-1] or best_results_gen[j] < best_results_gen[j-1] - 0.01 * best_results_gen[j-1]:
count += 1
# επιλογή γονέων
parents = np.zeros(population.shape)
for i in range(0, num_indiv):
parents[i, :] = functions.select_parents(population, fit, 0.75)
# crossover
children = functions.mate(parents.astype(int), crossrate)
# mutate
mutated = functions.mutate(children, mutrate)
population = mutated
population[0, :] = elit[0, :]
j += 1
if count > 5:
print(f'Μηδενική ή πολύ μικρή βελτίωση ατόμου, επόμενο τρέξιμο αλγορίθμου:')
gens = gen
break
gens = gen
# Μετά την ολοκλήρωση του for loop, θεωρητικά έχουμε το καλύτερο αποτέλεσμα πληθυσμού
fit = functions.fitness(population, x_test, y_test)
fittest = np.min(fit)
fitness_history[iter, gen+1] = fittest
gens += 1