def ga(pixel_arr, constraints, pop_size=100, g_max=40, p_c=0.6, p_m=0.4, verbose=True):
start_time = time.time()
num_rows, num_cols = pixel_arr.shape[0], pixel_arr.shape[1]
population = [Chromosome(pixel_arr) for i in range(pop_size)]
avg_fitness = sum(c.fitness for c in population)
best_ind = max(population, key=lambda c: c.fitness)
if verbose:
print("Generation 0:")
print("Best fitness:", best_ind.fitness, "Avg fitness:", avg_fitness)
print("Number of segmentations:", max(best_ind.segments))
print("Edge value:", best_ind.edge_value)
print("Connectivity:", best_ind.connectivity)
print("Deviation:", best_ind.deviation)
print(f"Initial generation time: {time.time() - start_time}\n", flush=True)
for g in range(1, g_max+1):
start_time = time.time()
offspring = []
for i in range(pop_size//2):
p1 = parent_selection(population)
p2 = parent_selection(population)
if random() < p_c:
cutoff = randint(0, len(p1.genotype)-1)
c1_geno = crossover(p1.genotype, p2.genotype, cutoff)
c2_geno = crossover(p2.genotype, p1.genotype, cutoff)
else:
c1_geno = p1.genotype
c2_geno = p2.genotype
if random() < p_m:
c1_geno = mutate(c1_geno, num_rows, num_cols, arr, constraints)
if random() < p_m:
c2_geno = mutate(c2_geno, num_rows, num_cols, arr, constraints)
c1 = Chromosome(pixel_arr, c1_geno)
c2 = Chromosome(pixel_arr, c2_geno)
offspring.append(c1)
offspring.append(c2)
population = elitist_replacement(population, offspring)
avg_fitness = sum(c.fitness for c in population)
best_ind = max(population, key=lambda c: c.fitness)
if verbose:
print(f"Generation {g}:")
print("Best fitness:", best_ind.fitness, "Avg fitness:", avg_fitness)
print("Number of segmentations:", max(best_ind.segments))
print("Edge value:", best_ind.edge_value)
print("Connectivity:", best_ind.connectivity)
print("Deviation:", best_ind.deviation)
print(f"Time elapsed: {time.time() - start_time}\n", flush=True)
return best_ind
def gsacrossover_population(M, chrs, temp):
c_ppl = select_for_crossover(chrs)
for _ in c_ppl:
minch = maxch = None
a = c_ppl.pop()
b = c_ppl.pop()
c = crossover(M, a, b)
if DEBUG: print "A: ", a
if DEBUG: print "B: ", b
if DEBUG: print "C: ", c
mutate_chromosome(c)
a_val=a.value()
b_val=b.value()
c_val=c.value()
if (a_val > b_val):
maxch = a
minch = b
else:
maxch = b
minch = a
if c_val < (maxch.value() + temp) :
# accept mutant to new population
chrs.append(c)
chrs.remove(maxch)
# otherwise we don't accept the mutant to
# the new population
assert len(chrs) == P_SIZE-1
def crossover_population(M, chrs):
c_ppl = select_for_crossover(chrs)
for _ in c_ppl:
a = c_ppl.pop()
b = c_ppl.pop()
c = crossover(M, a, b)
if DEBUG: print "A: ", a
if DEBUG: print "B: ", b
if DEBUG: print "C: ", c
mutate_chromosome(c)
chrs.append(c)
def createNew(withFitness):
global glassc
ranked = map(lambda t: t[1], sorted(withFitness, key=lambda t: t[0]))
best = ranked[:perGeneration / 4]
mutated = []
glassc += 1
for i in range(perGeneration - perGeneration / 4):
p1 = best[random.randint(0, len(best) - 1)]
p2 = best[random.randint(0, len(best) - 1)]
child1, child2 = chromosome.crossover(p1, p2)
child1 = child1.mutated()
child2 = child2.mutated()
mutated.append(child1)
return best + mutated
def createNew(withFitness):
global glassc
ranked = map(lambda t: t[1], sorted(withFitness, key=lambda t: t[0]))
best = ranked[: perGeneration / 4]
mutated = []
glassc += 1
for i in range(perGeneration - perGeneration / 4):
p1 = best[random.randint(0, len(best) - 1)]
p2 = best[random.randint(0, len(best) - 1)]
child1, child2 = chromosome.crossover(p1, p2)
child1 = child1.mutated()
child2 = child2.mutated()
mutated.append(child1)
return best + mutated
def mycross(ind1, ind2, gen_no):
child1 = crossover(ind1, ind2, gen_no, inputdim=indim, outputdim=outdim)
child2 = crossover(ind1, ind2, gen_no, inputdim=indim, outputdim=outdim)
return child1, child2
def mycross(ind1, ind2, gen_no):
child1 = crossover(ind1, ind2, gen_no, inputdim=8, outputdim=2)
child2 = crossover(ind1, ind2, gen_no, inputdim=8, outputdim=2)
return child1, child2
