dodanie algorytmu genetycznego

This commit is contained in:
Natalia Szymczak 2024-06-07 18:34:32 +02:00
parent 71e9bea0ba
commit 4b9a06b7ee
5 changed files with 184 additions and 25 deletions

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@ -6,7 +6,7 @@ class Constants:
def __init__(self): def __init__(self):
self.BLACK = (0, 0, 0) self.BLACK = (0, 0, 0)
self.RED = (255, 0, 0) self.RED = (255, 0, 0)
self.GRID_SIZE = 75 self.GRID_SIZE = 65
self.GRID_WIDTH = 30 self.GRID_WIDTH = 30
self.GRID_HEIGHT = 15 self.GRID_HEIGHT = 15
self.WINDOW_SIZE = (self.GRID_WIDTH * self.GRID_SIZE, self.GRID_HEIGHT * self.GRID_SIZE) self.WINDOW_SIZE = (self.GRID_WIDTH * self.GRID_SIZE, self.GRID_HEIGHT * self.GRID_SIZE)

148
genetics.py Normal file
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@ -0,0 +1,148 @@
from state_space_search import graphsearch, generate_cost_map
import random
# Parametry algorytmu genetycznego
POPULATION_SIZE = 700
MUTATION_RATE = 0.01
NUM_GENERATIONS = 600
# Generowanie początkowej populacji
def generate_individual(animals):
return random.sample(animals, len(animals))
def generate_population(animals, size):
return [generate_individual(animals) for _ in range(size)]
# Obliczanie odległości między zwierzetami
def calculate_distance(animal1, animal2):
x1, y1 = animal1
x2, y2 = animal2
return abs(x1 - x2) + abs(y1 - y2) # Odległość Manhattana
def calculate_total_distance(animals):
total_distance = 0
for i in range(len(animals) - 1):
total_distance += calculate_distance(animals[i], animals[i+1])
total_distance += calculate_distance(animals[-1], animals[0]) # Zamknięcie cyklu
return total_distance
# Selekcja rodziców za pomocą metody ruletki
def select_parents(population, num_parents):
fitness_scores = [1 / calculate_total_distance(individual) for individual in population]
total_fitness = sum(fitness_scores)
selection_probs = [fitness / total_fitness for fitness in fitness_scores]
parents = random.choices(population, weights=selection_probs, k=num_parents)
return parents
# Krzyżowanie rodziców (OX,Davis)
def crossover(parent1, parent2):
child1 = [None] * len(parent1)
child2 = [None] * len(parent1)
start_index = random.randint(0, len(parent1) - 1)
end_index = random.randint(start_index, len(parent1) - 1)
child1[start_index:end_index+1] = parent1[start_index:end_index+1]
child2[start_index:end_index+1] = parent2[start_index:end_index+1]
# Uzupełnienie brakujących zwierząt z drugiego rodzica
for i in range(len(parent1)):
if parent2[i] not in child1:
for j in range(len(parent2)):
if child1[j] is None:
child1[j] = parent2[i]
break
for i in range(len(parent1)):
if parent1[i] not in child2:
for j in range(len(parent1)):
if child2[j] is None:
child2[j] = parent1[i]
break
return child1, child2
# Mutacja: zamiana dwóch losowych zwierząt z prawdopodobieństwem MUTATION_RATE
def mutate(individual):
if random.random() < MUTATION_RATE:
index1, index2 = random.sample(range(len(individual)), 2)
individual[index1], individual[index2] = individual[index2], individual[index1]
# Algorytm genetyczny
def genetic_algorithm(animals):
population = generate_population(animals, POPULATION_SIZE)
for generation in range(NUM_GENERATIONS):
# Selekcja rodziców
parents = select_parents(population, POPULATION_SIZE // 2)
# Krzyżowanie i tworzenie nowej populacji
next_generation = []
for i in range(0, len(parents), 2):
parent1 = parents[i]
if i + 1 < len(parents):
parent2 = parents[i + 1]
else:
parent2 = parents[0]
child1, child2 = crossover(parent1, parent2)
next_generation.extend([child1, child2])
# Mutacja nowej populacji
for individual in next_generation:
mutate(individual)
# Zastąpienie starej populacji nową
population = next_generation
# Znalezienie najlepszego osobnika
best_individual = min(population, key=calculate_total_distance)
return best_individual
# def calculate_distance(start, goal, max_x, max_y, obstacles, cost_map):
# istate = (start[0], start[1], 'N') # Zakładamy, że zaczynamy od kierunku północnego
# actions, cost = graphsearch(istate, goal, max_x, max_y, obstacles, cost_map)
# return cost
# def calculate_total_distance(animals, max_x, max_y, obstacles, cost_map):
# total_distance = 0
# for i in range(len(animals) - 1):
# total_distance += calculate_distance(animals[i], animals[i+1], max_x, max_y, obstacles, cost_map)
# total_distance += calculate_distance(animals[-1], animals[0], max_x, max_y, obstacles, cost_map) # Zamknięcie cyklu
# return total_distance
# # Selekcja rodziców za pomocą metody ruletki
# def select_parents(population, num_parents, max_x, max_y, obstacles, cost_map):
# fitness_scores = [1 / calculate_total_distance(individual, max_x, max_y, obstacles, cost_map) for individual in population]
# total_fitness = sum(fitness_scores)
# selection_probs = [fitness / total_fitness for fitness in fitness_scores]
# parents = random.choices(population, weights=selection_probs, k=num_parents)
# return parents
# def genetic_algorithm(animals, max_x, max_y, obstacles, cost_map):
# population = generate_population(animals, POPULATION_SIZE)
# for generation in range(NUM_GENERATIONS):
# # Selekcja rodziców
# parents = select_parents(population, POPULATION_SIZE // 2, max_x, max_y, obstacles, cost_map)
# # Krzyżowanie i tworzenie nowej populacji
# next_generation = []
# for i in range(0, len(parents), 2):
# parent1 = parents[i]
# parent2 = parents[i + 1]
# child1, child2 = crossover(parent1, parent2)
# next_generation.extend([child1, child2])
# # Mutacja nowej populacji
# for individual in next_generation:
# mutate(individual)
# # Zastąpienie starej populacji nową
# population = next_generation
# # Znalezienie najlepszego osobnika
# best_individual = min(population, key=lambda individual: calculate_total_distance(individual, max_x, max_y, obstacles, cost_map))
# return best_individual

52
main.py
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@ -13,6 +13,7 @@ from constants import Constants, init_pygame
from draw import draw_goal, draw_grid, draw_house from draw import draw_goal, draw_grid, draw_house
from season import draw_background from season import draw_background
from night import change_time from night import change_time
from genetics import genetic_algorithm
const = Constants() const = Constants()
init_pygame(const) init_pygame(const)
@ -77,12 +78,13 @@ def main():
actions = [] actions = []
clock = pygame.time.Clock() clock = pygame.time.Clock()
spawned = False spawned = False
route = False
# Lista zawierająca klatki do odwiedzenia # # Lista zawierająca klatki do odwiedzenia
enclosures_to_visit = Enclosures.copy() # enclosures_to_visit = Enclosures.copy()
current_enclosure_index = -1 # Indeks bieżącej klatki # current_enclosure_index = -1 # Indeks bieżącej klatki
actions_to_compare_list = [] # Lista zawierająca ścieżki do porównania # actions_to_compare_list = [] # Lista zawierająca ścieżki do porównania
goals_to_compare_list = list() # Lista zawierająca cele do porównania # goals_to_compare_list = list() # Lista zawierająca cele do porównania
while True: while True:
for event in pygame.event.get(): for event in pygame.event.get():
@ -106,6 +108,11 @@ def main():
animal._feed = random.randint(0, 10) animal._feed = random.randint(0, 10)
spawned = True spawned = True
if not route:
animals = [(animal.x, animal.y) for animal in Animals]
best_route = genetic_algorithm(animals)
route = True
draw_Animals(Animals, const) draw_Animals(Animals, const)
draw_Terrain_Obstacles(Terrain_Obstacles, const) draw_Terrain_Obstacles(Terrain_Obstacles, const)
agent.draw(const) agent.draw(const)
@ -118,31 +125,34 @@ def main():
pygame.time.wait(200) pygame.time.wait(200)
else: else:
if agent._dryfood > 1 and agent._wetfood > 1 : if agent._dryfood > 1 and agent._wetfood > 1 :
if not goals_to_compare_list: # if not goals_to_compare_list:
current_enclosure_index = (current_enclosure_index + 1) % len(enclosures_to_visit) # current_enclosure_index = (current_enclosure_index + 1) % len(enclosures_to_visit)
current_enclosure = enclosures_to_visit[current_enclosure_index] # current_enclosure = enclosures_to_visit[current_enclosure_index]
for animal in current_enclosure.animals: # for animal in current_enclosure.animals:
goal = (animal.x, animal.y) # goal = (animal.x, animal.y)
goals_to_compare_list.append(goal) # goals_to_compare_list.append(goal)
actions_to_compare = graphsearch(agent.istate, goal, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, cost_map) # actions_to_compare = graphsearch(agent.istate, goal, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, cost_map)
actions_to_compare_list.append((actions_to_compare, goal)) # actions_to_compare_list.append((actions_to_compare, goal))
chosen_path_and_goal = min(actions_to_compare_list, key=lambda x: len(x[0])) # chosen_path_and_goal = min(actions_to_compare_list, key=lambda x: len(x[0]))
goal = chosen_path_and_goal[1] # goal = chosen_path_and_goal[1]
# draw_goal(const, goal)
# # Usuń wybrany element z listy
# actions_to_compare_list.remove(chosen_path_and_goal)
# goals_to_compare_list.remove(goal)
goal = best_route.pop(0)
best_route.append(goal)
draw_goal(const, goal) draw_goal(const, goal)
# Usuń wybrany element z listy actions, cost = graphsearch(agent.istate, goal, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, cost_map)
actions_to_compare_list.remove(chosen_path_and_goal)
goals_to_compare_list.remove(goal)
actions = graphsearch(agent.istate, goal, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, cost_map)
else: else:
goal = (3,1) goal = (3,1)
draw_goal(const, goal) draw_goal(const, goal)
actions = graphsearch(agent.istate, goal, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, cost_map) actions, cost = graphsearch(agent.istate, goal, const.GRID_WIDTH, const.GRID_HEIGHT, obstacles, cost_map)
if __name__ == "__main__": if __name__ == "__main__":
main() main()

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@ -40,7 +40,7 @@ def graphsearch(istate, goal, max_x, max_y, obstacles, cost_map):
state, _, _ = node state, _, _ = node
if goaltest(state, goal): if goaltest(state, goal):
return build_action_sequence(node) return build_action_sequence(node), current_cost(node, cost_map)
explored.add(state) explored.add(state)
@ -61,7 +61,7 @@ def graphsearch(istate, goal, max_x, max_y, obstacles, cost_map):
else: else:
break break
return False return False, float('inf')
def is_state_in_queue(state, queue): def is_state_in_queue(state, queue):
for _, (s, _, _) in queue.queue: for _, (s, _, _) in queue.queue:
@ -125,3 +125,4 @@ def generate_cost_map(Animals, Terrain_Obstacles, cost_map={}):
cost_map[(terrain_obstacle.x , terrain_obstacle.y )] = bush_cost cost_map[(terrain_obstacle.x , terrain_obstacle.y )] = bush_cost
return cost_map return cost_map

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