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algo.py
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algo.py
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from random import (choice, random, randint)
import view
__all__ = ['Chromosome', 'Population']
class Chromosome:
"""
This class is used to define a chromosome for the gentic algorithm
simulation.
This class is essentially nothing more than a container for the details
of the chromosome, namely the gene (the string that represents our
target string) and the fitness (how close the gene is to the target
string).
Note that this class is immutable. Calling mate() or mutate() will
result in a new chromosome instance being created.
"""
# [leg height, leg width]
gene_range = [[50, 100], [50, 100], [50, 100]]
def __init__(self, gene):
self.gene = gene
self.fitness = 0
self.name = self.get_name
def mate(self, mate):
"""
Method used to mate the chromosome with another chromosome,
resulting in a new chromosome being returned.
"""
# random int on the range of length of gene
pivot = randint(0, len(self.gene) - 1)
# generates two mismatched genes
gene1 = self.gene[:pivot] + mate.gene[pivot:]
gene2 = mate.gene[:pivot] + self.gene[pivot:]
return Chromosome(gene1), Chromosome(gene2)
def mutate(self):
"""
Method used to generate a new chromosome based on a change in a
random character in the gene of this chromosome. A new chromosome
will be created, but this original will not be affected.
"""
# randomly change a character of the chromosome
gene = self.gene
idx = randint(0, len(gene) - 1)
delta = randint(-int(gene[idx]/2),int(gene[idx]/2))
gene[idx] += delta
return Chromosome(gene)
@staticmethod
def _update_fitness(gene):
"""
Helper method used to return the fitness for the chromosome based
on its gene.
"""
fitness = 0
for a, b in zip(gene, Chromosome._target_gene):
fitness += abs(a - b)
return fitness
@staticmethod
def gen_random():
"""
A convenience method for generating a random chromosome with a random
gene.
"""
gene = []
for min_val, max_val in Chromosome.gene_range:
gene.append(randint(min_val, max_val)/50.0)
return gene
def get_name():
return ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(10))
class Population:
"""
A class representing a population for a genetic algorithm simulation.
A population is simply a sorted collection of chromosomes
(sorted by fitness) that has a convenience method for evolution. This
implementation of a population uses a tournament selection algorithm for
selecting parents for crossover during each generation's evolution.
Note that this object is mutable, and calls to the evolve()
method will generate a new collection of chromosome objects.
"""
_tournamentSize = 3
def __init__(self, size=2084, crossover=0.8, elitism=0.1, mutation=0.03):
self.elitism = elitism
self.mutation = mutation
self.crossover = crossover
buf = []
for i in range(size): buf.append(Chromosome(Chromosome.gen_random()))
self.population = list(sorted(buf, key=lambda x: x.fitness))
def _tournament_selection(self):
"""
A helper method used to select a random chromosome from the
population using a tournament selection algorithm.
"""
best = choice(self.population)
for i in range(Population._tournamentSize):
cont = choice(self.population)
if (cont.fitness > best.fitness): best = cont
return best
def _selectParents(self):
"""
A helper method used to select two parents from the population using a
tournament selection algorithm.
"""
return (self._tournament_selection(), self._tournament_selection())
def evolve(self):
"""
Method to evolve the population of chromosomes.
"""
size = len(self.population)
idx = int(round(size * self.elitism))
buf = self.population[:idx]
while (idx < size):
if random() <= self.crossover:
(p1, p2) = self._selectParents()
children = p1.mate(p2)
for c in children:
if random() <= self.mutation:
buf.append(c.mutate())
else:
buf.append(c)
idx += 2
else:
if random() <= self.mutation:
buf.append(self.population[idx].mutate())
else:
buf.append(self.population[idx])
idx += 1
self.population = list(sorted(buf[:size], key=lambda x: x.fitness))
"""
if __name__ == "__main__":
maxGenerations = 16384
size = 20
pop = Population(size=size, crossover=0.8, elitism=0.2, mutation=0.3)
for i in range(1, maxGenerations + 1):
print("Generation %d:" % i)
total_fitness = 0
for c in range(size):
pop.population[c].fitness = view.create(pop.population[c].gene[0]*.3, pop.population[c].gene[1]*.3)
print("Character", c, "=", pop.population[c].fitness)
total_fitness += pop.population[c].fitness
else:
print("Average Fitness of population", i, "=", total_fitness/size)
pop.population = list(sorted(pop.population, key=lambda x: x.fitness))
print([pop.population[i].fitness for i in range(size)])
pop.evolve()
else:
print("Maximum generations reached without success.")
"""