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4e640ffc2d
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c2cc3c716d
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@ -1,11 +1,10 @@
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from numpy import intersect1d, array_equal
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from numpy import sum, append, intersect1d, array_equal
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from numpy.random import randint, choice, shuffle
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from numpy.random import randint, choice, shuffle
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from pandas import DataFrame
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from pandas import DataFrame
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from math import ceil
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from math import ceil
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from functools import partial
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from functools import partial
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from multiprocessing import Pool
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from multiprocessing import Pool
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from copy import deepcopy
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from copy import deepcopy
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from itertools import combinations
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def get_row_distance(source, destination, data):
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def get_row_distance(source, destination, data):
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@ -36,23 +35,22 @@ def generate_individual(n, m, data):
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def evaluate_individual(individual, data):
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def evaluate_individual(individual, data):
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fitness = 0
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fitness = []
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comb = combinations(individual.index, r=2)
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genotype = individual.point.values
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for index in list(comb):
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distances = data.query(f"source in @genotype and destination in @genotype")
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elements = individual.loc[index, :]
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for item in genotype[:-1]:
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fitness += get_row_distance(
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element_df = distances.query(f"source == {item} or destination == {item}")
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source=elements["point"].head(n=1).values[0],
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max_distance = element_df["distance"].astype(float).max()
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destination=elements["point"].tail(n=1).values[0],
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fitness = append(arr=fitness, values=max_distance)
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data=data,
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distances = distances.query(f"source != {item} and destination != {item}")
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)
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individual["fitness"] = sum(fitness)
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individual["fitness"] = fitness
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return individual
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return individual
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def select_distinct_genes(matching_genes, parents, m):
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def select_distinct_genes(matching_genes, parents, m):
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first_parent = parents[0].query("point not in @matching_genes")
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first_parent = parents[0].query("point not in @matching_genes")
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second_parent = parents[1].query("point not in @matching_genes")
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second_parent = parents[1].query("point not in @matching_genes")
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cutoff = randint(m - len(matching_genes) + 1)
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cutoff = randint(m - len(matching_genes))
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first_parent_genes = first_parent.point.values[cutoff:]
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first_parent_genes = first_parent.point.values[cutoff:]
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second_parent_genes = second_parent.point.values[:cutoff]
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second_parent_genes = second_parent.point.values[:cutoff]
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return first_parent_genes, second_parent_genes
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return first_parent_genes, second_parent_genes
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@ -139,8 +137,9 @@ def group_parents(parents):
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first = parents[i]
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first = parents[i]
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second = parents[i + 1]
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second = parents[i + 1]
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if array_equal(first.point.values, second.point.values):
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if array_equal(first.point.values, second.point.values):
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random_index = randint(i + 1)
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tmp = second
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second, parents[random_index] = parents[random_index], second
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second = parents[i - 2]
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parents[i - 2] = tmp
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parent_pairs.append([first, second])
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parent_pairs.append([first, second])
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return parent_pairs
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return parent_pairs
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43
src/main.py
43
src/main.py
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@ -1,7 +1,9 @@
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from preprocessing import parse_file
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from preprocessing import parse_file
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from genetic_algorithm import genetic_algorithm
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from genetic_algorithm import genetic_algorithm
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from memetic_algorithm import memetic_algorithm
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from memetic_algorithm import memetic_algorithm
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from sys import argv
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from time import time
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from time import time
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from itertools import combinations
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from argparse import ArgumentParser
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from argparse import ArgumentParser
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@ -15,6 +17,7 @@ def execute_algorithm(args, n, m, data):
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crossover_mode=args.crossover,
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crossover_mode=args.crossover,
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max_iterations=100,
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max_iterations=100,
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)
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)
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else:
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return memetic_algorithm(
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return memetic_algorithm(
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n,
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n,
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m,
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m,
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@ -24,15 +27,44 @@ def execute_algorithm(args, n, m, data):
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)
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)
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def show_results(solution, time_delta):
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def get_row_distance(source, destination, data):
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duplicates = solution.duplicated().any()
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row = data.query(
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print(solution)
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"""(source == @source and destination == @destination) or \
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print(f"Total distance: {solution.fitness.values[0]}")
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(source == @destination and destination == @source)"""
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)
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return row["distance"].values[0]
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def get_fitness(solutions, data):
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counter = 0
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comb = combinations(solutions.index, r=2)
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for index in list(comb):
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elements = solutions.loc[index, :]
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counter += get_row_distance(
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source=elements["point"].head(n=1).values[0],
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destination=elements["point"].tail(n=1).values[0],
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data=data,
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)
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return counter
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def show_results(solutions, fitness, time_delta):
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duplicates = solutions.duplicated().any()
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print(solutions)
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print(f"Total distance: {fitness}")
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if not duplicates:
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if not duplicates:
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print("No duplicates found")
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print("No duplicates found")
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print(f"Execution time: {time_delta}")
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print(f"Execution time: {time_delta}")
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def usage(argv):
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print(f"Usage: python {argv[0]} <file> <algorithm choice> <")
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print("algorithm choices:")
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print("genetic: genetic algorithm")
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print("memetic: memetic algorithm")
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exit(1)
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def parse_arguments():
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def parse_arguments():
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parser = ArgumentParser()
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parser = ArgumentParser()
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parser.add_argument("file", help="dataset of choice")
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parser.add_argument("file", help="dataset of choice")
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@ -51,7 +83,8 @@ def main():
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start_time = time()
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start_time = time()
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solutions = execute_algorithm(args, n, m, data)
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solutions = execute_algorithm(args, n, m, data)
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end_time = time()
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end_time = time()
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show_results(solutions, time_delta=end_time - start_time)
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fitness = get_fitness(solutions, data)
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show_results(solutions, fitness, time_delta=end_time - start_time)
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if __name__ == "__main__":
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if __name__ == "__main__":
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@ -1,22 +1,50 @@
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from genetic_algorithm import *
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from numpy.random import choice, seed
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from local_search import local_search
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def run_local_search(n, m, data, individual):
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def get_first_random_solution(m, data):
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pass
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seed(42)
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random_indexes = choice(len(data.index), size=m, replace=False)
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return data.loc[random_indexes]
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def memetic_algorithm(n, m, data, hybridation, max_iterations=100000):
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def element_in_dataframe(solution, element):
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population = [generate_individual(n, m, data) for _ in range(n)]
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duplicates = solution.query(
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population = evaluate_population(population, data)
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f"(source == {element.source} and destination == {element.destination}) or (source == {element.destination} and destination == {element.source})"
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for i in range(max_iterations):
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)
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if i % 10 == 0:
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return not duplicates.empty
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best_index, _ = get_best_elements(population)
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run_local_search(n, m, data, individual=population[best_index])
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parents = select_parents(population, n, mode="stationary")
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def replace_worst_element(previous, data):
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offspring = crossover(mode="uniform", parents=parents, m=m)
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solution = previous.copy()
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offspring = mutate(offspring, n, data)
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worst_index = solution["distance"].astype(float).idxmin()
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population = replace_population(population, offspring, mode="stationary")
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random_element = data.sample().squeeze()
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population = evaluate_population(population, data)
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while element_in_dataframe(solution=solution, element=random_element):
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best_index, _ = get_best_elements(population)
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random_element = data.sample().squeeze()
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return population[best_index]
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solution.loc[worst_index] = random_element
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return solution, worst_index
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def get_random_solution(previous, data):
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solution, worst_index = replace_worst_element(previous, data)
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previous_worst_distance = previous["distance"].loc[worst_index]
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while solution.distance.loc[worst_index] <= previous_worst_distance:
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solution, _ = replace_worst_element(previous=solution, data=data)
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return solution
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def explore_neighbourhood(element, data, max_iterations=100000):
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neighbourhood = []
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neighbourhood.append(element)
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for _ in range(max_iterations):
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previous_solution = neighbourhood[-1]
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neighbour = get_random_solution(previous=previous_solution, data=data)
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neighbourhood.append(neighbour)
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return neighbour
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def memetic_algorithm(m, data):
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first_solution = get_first_random_solution(m=m, data=data)
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best_solution = explore_neighbourhood(
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element=first_solution, data=data, max_iterations=100
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)
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return best_solution
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