WMICraft/algorithms/genetic/population.py

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()