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Genetics Algorithms

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s473633 2023-06-19 11:25:43 +02:00
parent f96f8229a9
commit e7358998ef
9 changed files with 353 additions and 1 deletions
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src/agent.py
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@ -3,6 +3,7 @@ import pygame as pg
import heapq
from decisiontree import decision
from trashtype import trash_type_def
import genetics
HOUSE_NUMBER = {'House_number': 0}
@ -40,6 +41,7 @@ class HousePOI:
self.trash = 1
self.dump_fullness = 0
def discover_bins(self, bin_fullness, trash_type, payment):
from simulation import Interface
@ -146,6 +148,7 @@ class Agent:
self.fullness = None
self.weights = {}
self.orientation = 90
self.genetic_house_list = None
# utworzenie grafu dróg
roads_pos = [vector_to_tuple(pos) for pos in
@ -299,7 +302,8 @@ class Agent:
def a_star(self):
fringe = []
explored = set()
initial_state = (self.current_pos, tuple(self.houses))
self.genetic_house_list = genetics.genetic_algorithm(sorted(self.houses))
initial_state = (self.current_pos, tuple(self.genetic_house_list))
goal_state = (self.current_pos, ())
start_node = Node(initial_state, None, 0)

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src/genetics.py Normal file
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@ -0,0 +1,95 @@
import random
# Parametry algorytmu genetycznego
POPULATION_SIZE = 100
MUTATION_RATE = 0.01
NUM_GENERATIONS = 100
# Generowanie początkowej populacji
def generate_individual(houses):
return random.sample(houses, len(houses))
def generate_population(houses, size):
return [generate_individual(houses) for _ in range(size)]
# Obliczanie odległości między domkami
def calculate_distance(house1, house2):
x1, y1 = house1
x2, y2 = house2
return abs(x1 - x2) + abs(y1 - y2)
def calculate_total_distance(houses):
total_distance = 0
for i in range(len(houses) - 1):
total_distance += calculate_distance(houses[i], houses[i+1])
total_distance += calculate_distance(houses[-1], houses[0]) # Zamknięcie cyklu
return total_distance
# Selekcja rodziców za pomocą turniejowej metody
def select_parents(population, num_parents):
parents = []
for _ in range(num_parents):
tournament = random.sample(population, 5)
winner = min(tournament, key=calculate_total_distance)
parents.append(winner)
return parents
# Krzyżowanie rodziców
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 domków 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 domków
def mutate(individual):
index1, index2 = random.sample(range(len(individual)), 2)
individual[index1], individual[index2] = individual[index2], individual[index1]
# Algorytm genetyczny
def genetic_algorithm(houses):
population = generate_population(houses, POPULATION_SIZE)
for _ in range(NUM_GENERATIONS):
# Obliczanie wartości przystosowania (odległości) dla każdego osobnika
fitness_scores = [calculate_total_distance(individual) for individual in population]
# Wybór rodziców do reprodukcji
parents = select_parents(population, 2)
# Tworzenie nowej populacji za pomocą krzyżowania i mutacji
new_population = []
for i in range(POPULATION_SIZE // 2):
child1, child2 = crossover(parents[0], parents[1])
mutate(child1)
mutate(child2)
new_population.extend([child1, child2])
population = new_population
# Znalezienie najlepszego osobnika
best_individual = min(population, key=calculate_total_distance)
return best_individual

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src/tree_data.csv Normal file
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@ -0,0 +1,201 @@
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1 dump_fullness truck_fullness trash payment bin_fullness trash_types season day decision
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177 0 0 1 1 1 paper summer wednesday 0
178 1 0 1 0 1 paper autumn wednesday 0
179 0 1 1 1 1 mixed spring friday 0
180 0 0 0 1 1 glass winter friday 0
181 0 1 0 0 0 glass winter monday 0
182 0 0 1 0 1 glass autumn wednesday 0
183 0 0 0 1 0 plastic summer friday 0
184 1 1 1 1 0 mixed spring wednesday 0
185 1 0 1 0 1 plastic autumn friday 0
186 0 1 1 0 1 mixed autumn monday 0
187 0 1 0 1 1 plastic winter wednesday 0
188 0 1 1 0 1 mixed winter friday 0
189 1 0 0 0 1 plastic summer friday 0
190 1 0 1 0 1 mixed spring friday 0
191 1 1 1 1 0 paper summer monday 0
192 1 0 1 0 1 paper winter friday 0
193 0 1 1 0 1 paper summer friday 0
194 0 0 1 1 1 plastic winter wednesday 1
195 0 0 1 1 1 mixed spring monday 0
196 0 1 1 0 1 paper winter wednesday 0
197 1 1 1 1 0 plastic spring friday 0
198 1 0 1 1 0 paper summer friday 0
199 1 1 0 0 1 glass autumn friday 0
200 0 0 1 1 1 glass spring wednesday 1
201 1 1 0 1 1 paper summer friday 0

52
src/tree_in_txt.txt Normal file
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@ -0,0 +1,52 @@
|--- dump_fullness <= 0.50
| |--- truck_fullness <= 0.50
| | |--- trash <= 0.50
| | | |--- class: 0
| | |--- trash > 0.50
| | | |--- payment <= 0.50
| | | | |--- class: 0
| | | |--- payment > 0.50
| | | | |--- bin_fullness <= 0.50
| | | | | |--- class: 0
| | | | |--- bin_fullness > 0.50
| | | | | |--- trash_types_mixed <= 0.50
| | | | | | |--- season_summer <= 0.50
| | | | | | | |--- trash_types_plastic <= 0.50
| | | | | | | | |--- class: 1
| | | | | | | |--- trash_types_plastic > 0.50
| | | | | | | | |--- season_winter <= 0.50
| | | | | | | | | |--- class: 1
| | | | | | | | |--- season_winter > 0.50
| | | | | | | | | |--- day_wednesday <= 0.50
| | | | | | | | | | |--- class: 0
| | | | | | | | | |--- day_wednesday > 0.50
| | | | | | | | | | |--- class: 1
| | | | | | |--- season_summer > 0.50
| | | | | | | |--- day_wednesday <= 0.50
| | | | | | | | |--- class: 1
| | | | | | | |--- day_wednesday > 0.50
| | | | | | | | |--- trash_types_plastic <= 0.50
| | | | | | | | | |--- class: 0
| | | | | | | | |--- trash_types_plastic > 0.50
| | | | | | | | | |--- class: 1
| | | | | |--- trash_types_mixed > 0.50
| | | | | | |--- day_wednesday <= 0.50
| | | | | | | |--- season_winter <= 0.50
| | | | | | | | |--- day_monday <= 0.50
| | | | | | | | | |--- season_spring <= 0.50
| | | | | | | | | | |--- class: 0
| | | | | | | | | |--- season_spring > 0.50
| | | | | | | | | | |--- class: 1
| | | | | | | | |--- day_monday > 0.50
| | | | | | | | | |--- season_spring <= 0.50
| | | | | | | | | | |--- class: 1
| | | | | | | | | |--- season_spring > 0.50
| | | | | | | | | | |--- class: 0
| | | | | | | |--- season_winter > 0.50
| | | | | | | | |--- class: 0
| | | | | | |--- day_wednesday > 0.50
| | | | | | | |--- class: 1
| |--- truck_fullness > 0.50
| | |--- class: 0
|--- dump_fullness > 0.50
| |--- class: 0