train.py

# Space Discover: Genetic Algorithms - Training File

# Importing the libraries
import numpy as np
from environment import Environment


# Creating the bots
class Route():
    def __init__(self, dnaLength):
        self.dnaLength = dnaLength
        self.dna = list()
        self.distance = 0

        # Initialize the random dna
        for i in range(self.dnaLength - 1):
            rnd = np.random.randint(1, self.dnaLength)
            while rnd in self.dna:
                rnd = np.random.randint(1, self.dnaLength)
            self.dna.append(rnd)
        self.dna.append(0)

    # Building the Crossover method
    def mix(self, dna1, dna2):
        self.dna = dna1.copy()

        for i in range(self.dnaLength - 1):
            if np.random.rand() <= 0.5:
                previous = self.dna[i]
                inx = self.dna.index(dna2[i])
                self.dna[inx] = previous
                self.dna[i] = dna2[i]

        # Random Partial Mutations 1
        for i in range(self.dnaLength - 1):
            if np.random.rand() <= 0.1:
                previous = self.dna[i]
                rnd = np.random.randint(1, self.dnaLength)
                inx = self.dna.index(rnd)
                self.dna[inx] = previous
                self.dna[i] = rnd

            # Random Partial Mutations 2
            elif np.random.rand() <= 0.1:
                rnd = np.random.randint(1, self.dnaLength)
                prevInx = self.dna.index(rnd)
                self.dna.insert(i, rnd)

                if i >= prevInx:
                    self.dna.pop(prevInx)
                else:
                    self.dna.pop(prevInx + 1)


# Initializing the main code
populationSize = 50
mutationRate = 0.1
nSelected = 5

env = Environment()
dnaLength = len(env.planets)
population = list()

# Creating the first population
for i in range(populationSize):
    route = Route(dnaLength)
    population.append(route)

# Starting the main loop
generation = 0
bestDist = np.inf
while True:
    generation += 1

    # Evaluating the population
    for route in population:
        env.reset()

        for i in range(dnaLength):
            action = route.dna[i]

            route.distance += env.step(action, "none")

    # Sorting the population
    sortedPop = sorted(population, key=lambda x: x.distance)
    population.clear()

    if sortedPop[0].distance < bestDist:
        bestDist = sortedPop[0].distance

    # Adding best previous bots to the population
    for i in range(nSelected):
        best = sortedPop[i]
        best.distance = 0
        population.append(best)

    # Filling in the rest of the population
    left = populationSize - nSelected

    for i in range(left):
        newRoute = Route(dnaLength)
        if np.random.rand() <= mutationRate:
            population.append(newRoute)
        else:
            inx1 = np.random.randint(0, nSelected)
            inx2 = np.random.randint(0, nSelected)
            while inx1 == inx2:
                inx2 = np.random.randint(0, nSelected)

            dna1 = sortedPop[inx1].dna
            dna2 = sortedPop[inx2].dna

            newRoute.mix(dna1, dna2)

            population.append(newRoute)

    # Displaying the results
    env.reset()

    for i in range(dnaLength):
        action = sortedPop[0].dna[i]
        _ = env.step(action, "normal")

    if generation % 100 == 0:
        env.reset()

        for i in range(dnaLength):
            action = sortedPop[0].dna[i]
            _ = env.step(action, "beautiful")

    print("Generation: " + str(generation) +
          " Shortest Distance: {:.2f}".format(bestDist) +
          " light years")