SI_2020/genetic_route.py

import numpy as np
from random import randrange, random
from math import floor
import copy

num_of_surviving = 6
num_of_couples = 8
mutation_probability = 0.07
max_population = 20
iterations = 50

# creates new random solution to add to population
def create_new_route(points):
    route = np.random.permutation(points)
    route = [x + 1 for x in route]
    return route

# creates initian population
def create_population(points):
    population = []
    for i in range(max_population):
        population.append(create_new_route(points))
    return population

# gives score to a solution based on lenght
def score_route(graph_map, route):
    score = graph_map[0][route[0]]
    for i in range(len(route) - 2):
        rack = len(route) + route[0]
        score = score + graph_map[rack][route[i + 1]]
        score = score + graph_map[route[i + 1]][route[i + 2]]
    return score

# scores every solution in population
def score_all(graph_map, population):
    scores = []
    for i in range(len(population)):
        tmp = [i, score_route(graph_map, population[i])]
        scores.append(tmp)
    return scores

# designed to create new population by mixing steps between most succesfull solutions
def crossover(a, b):
    new_a = copy.deepcopy(a)
    new_b = copy.deepcopy(b)
    for i in range(floor(len(a) / 2)):
        rel = randrange(len(a))
        tmp_a = new_a[rel]
        tmp_b = new_b[rel]
        if tmp_a == tmp_b:
            continue
        new_a[new_a.index(tmp_b)] = tmp_a
        new_b[new_b.index(tmp_a)] = tmp_b
        new_a[rel] = tmp_b
        new_b[rel] = tmp_a
    return new_a, new_b

# adds randomness to newly created solutions
def mutate(route):
    new_route = copy.deepcopy(route)
    for i in range(len(route) - 1):
        if random() < mutation_probability:
            tmp = new_route[i]
            new_route[i] = new_route[i + 1]
            new_route[i + 1] = tmp
    return new_route

# main function that iterate population until the best solutions emerge
def genetic_trace_route(graph_map, packages):
    population = create_population(packages)
    for i in range(iterations):
        new_population = []
        scores = score_all(graph_map, population)
        scores.sort(key=lambda x: x[1])
# breeding
        for j in range(0, num_of_couples, 2):
            a, b = crossover(population[scores[j][0]], population[scores[j+1][0]])
            new_population.append(a)
            new_population.append(b)
# mutations
        for j in range(len(new_population)):
            mutate(new_population[j])
# survival
        for j in range(0, num_of_surviving):
            new_population.append(population[scores[j][0]])
        # random new
        for j in range(max_population - (num_of_surviving + num_of_couples)):
            new_population.append(create_new_route(packages))
        population.clear()
        population = copy.deepcopy(new_population)
        scores = score_all(graph_map, population)
        scores.sort(key=lambda x: x[1])
        print("Best route of all population in iteration " + i + 1)
        print(scores[0][1])

    scores = score_all(graph_map, population)
    scores.sort(key=lambda x: x[1])
    return population[scores[0][0]]