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Jan 3, 2018
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Search: Update Genetic Algorithm To New Pseudocode #676

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29 changes: 12 additions & 17 deletions search.py
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Original file line number Diff line number Diff line change
Expand Up @@ -702,20 +702,11 @@ def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20):
return genetic_algorithm(states[:n], problem.value, ngen, pmut)


def genetic_algorithm(population, fitness_fn, gene_pool=[0, 1], f_thres=None, ngen=1000, pmut=0.1): # noqa
def genetic_algorithm(population, fitness_fn, gene_pool=[0, 1], f_thres=None, ngen=1000, pmut=0.1):
"""[Figure 4.8]"""
for i in range(ngen):
new_population = []
random_selection = selection_chances(fitness_fn, population)
for j in range(len(population)):
x = random_selection()
y = random_selection()
child = reproduce(x, y)
if random.uniform(0, 1) < pmut:
child = mutate(child, gene_pool)
new_population.append(child)

population = new_population
population = [mutate(recombine(*select(2, population, fitness_fn)), gene_pool, pmut)
for i in range(len(population))]

if f_thres:
fittest_individual = argmax(population, key=fitness_fn)
Expand All @@ -739,18 +730,22 @@ def init_population(pop_number, gene_pool, state_length):
return population


def selection_chances(fitness_fn, population):
def select(r, population, fitness_fn):
fitnesses = map(fitness_fn, population)
return weighted_sampler(population, fitnesses)
sampler = weighted_sampler(population, fitnesses)
return [sampler() for i in range(r)]


def reproduce(x, y):
def recombine(x, y):
n = len(x)
c = random.randrange(1, n)
c = random.randrange(0, n)
return x[:c] + y[c:]


def mutate(x, gene_pool):
def mutate(x, gene_pool, pmut):
if random.uniform(0, 1) >= pmut:
return x

n = len(x)
g = len(gene_pool)
c = random.randrange(0, n)
Expand Down