WMICraft/algorithms/genetic/population.py

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2022-06-02 00:52:16 +02:00
import random
from typing import List
import numpy as np
import numpy.typing as npt
from genome import Genome
class Population:
population: List[Genome] = [] # array to hold the current population
mating_pool: List[Genome] = [] # array which we will use for our "mating pool"
generations: int = 0 # number of generations
finished: bool = False # are we finished evolving?
mutation_rate: float
perfect_score: int
best_genome: Genome
def __init__(self, mutation_rate, population_size, perfect_score=20):
self.mutation_rate = mutation_rate
self.perfect_score = perfect_score
for i in range(0, population_size):
new_genome = Genome()
new_genome.calc_fitness()
self.population.append(new_genome)
# create a new generation
def generate(self):
max_fitness = 0
for genome in self.population:
if genome.fitness > max_fitness:
max_fitness = genome.fitness
print("Max fitness of generation " + str(self.generations) + " = " + str(max_fitness))
# refill the population with children from the mating pool
new_population = []
for genome in self.population:
partner_a = self.accept_reject(max_fitness)
partner_b = self.accept_reject(max_fitness)
child = partner_a.crossover(partner_b)
child.mutate(self.mutation_rate)
new_population.append(child)
self.population = new_population
self.generations += 1
# select random with correct probability from population
def accept_reject(self, max_fitness: int):
safe_flag = 0
while safe_flag < 10000:
partner = random.choice(self.population)
r = random.randint(0, max_fitness)
if r < partner.fitness:
return partner
safe_flag += 1
# compute the current "most fit" member of the population
def evaluate(self):
record = 0
best_index = 0
for index in range(len(self.population)):
genome = self.population[index]
if genome.fitness > record:
record = genome.fitness
best_index = index
self.best_genome = self.population[best_index]
if record >= self.perfect_score:
self.finished = True
return self.finished
def calc_fitness(self):
for genome in self.population:
genome.calc_fitness()