mutations geneticAlg

AI_brain/genetic_algorytm.py

@@ -1,6 +1,9 @@
+import copy
 import random
 import configparser
 import math
+
+import pygame
 from domain.entities.entity import Entity
 
 
@@ -9,6 +12,7 @@ config.read("config.ini")
 
 from domain.world import World
 from AI_brain.rotate_and_go_aStar import RotateAndGoAStar, State
+from random import randint
 
 
 hits = 0
@@ -44,9 +48,7 @@ class Path:
         permutation = generate_random_permutation(len(dusts))
         self.permutation = permutation
 
-        self.walk = addStartAndStation(
-            permutation, config.getint("CONSTANT", "BananaFilling")
-        )
+        self.walk = addStartAndStation(permutation)
 
     def calculate_distance(self, world: World):
         distance = 0
@@ -145,8 +147,8 @@ def generate_random_permutation(n):
 
 
 # BUG solution: inverse direction at the last step
-def addStartAndStation(permutation: list[int], bananaFilling: int):
-    frequency = math.ceil(100 / bananaFilling)
+def addStartAndStation(permutation: list[int]):
+    frequency = math.ceil(100 / config.getint("CONSTANT", "BananaFilling"))
     numer_of_stops = math.ceil(len(permutation) / frequency)
     walk = permutation.copy()
 
@@ -186,6 +188,16 @@ class GeneticAlgorytm:
             path.calculate_distance(self.world)
             self.paths.append(path)
 
+    def print_top(self):
+        print(
+            "Best path: ",
+            self.best_path.walk,
+            "Distance: ",
+            self.best_path.distance,
+        )
+        for path in self.paths[1:]:
+            print(path.walk, path.distance)
+
     def evaluate_population(self):
         self.paths.sort(key=lambda x: x.distance, reverse=False)
 
@@ -207,9 +219,7 @@ class GeneticAlgorytm:
             if item not in child.permutation:
                 child.permutation.append(item)
 
-        child.walk = addStartAndStation(
-            child.permutation, config.getint("CONSTANT", "BananaFilling")
-        )
+        child.walk = addStartAndStation(child.permutation)
 
         child.calculate_distance(self.world)
 
@@ -220,7 +230,6 @@ class GeneticAlgorytm:
 
         for i in range(self.iteration_number):
             self.crossover()
-            # self.mutate()
 
         self.evaluate_population()
         self.best_real_path = self.paths[0].get_real_path(self.world)
@@ -229,6 +238,25 @@ class GeneticAlgorytm:
 
         print(hits / (misses + hits))
 
+    def mutate(self, mutant: Path) -> Path:
+        random_number = randint(0, len(mutant.permutation) - 1)
+        random_number2 = random_number
+        while random_number == random_number2:
+            random_number2 = randint(0, len(mutant.permutation) - 1)
+
+        mutant.permutation[random_number], mutant.permutation[random_number2] = (
+            mutant.permutation[random_number2],
+            mutant.permutation[random_number],
+        )
+
+        if tuple(mutant.permutation) in self.checked_permutations:
+            return self.mutate(mutant)
+
+        mutant.walk = addStartAndStation(mutant.permutation)
+
+        mutant.calculate_distance(self.world)
+        return mutant
+
     def crossover(self):
         for i in range(self.descendants_number):
             parent1 = self.paths[random.randint(0, self.population_size - 1)]
@@ -244,4 +272,10 @@ class GeneticAlgorytm:
             self.checked_permutations[tuple(child.permutation)] = True
             self.paths.append(child)
 
+            mutant = Path()
+            mutant.permutation = child.permutation.copy()
+            mutant = self.mutate(mutant)
+            self.checked_permutations[tuple(mutant.permutation)] = True
+            self.paths.append(mutant)
+
         self.evaluate_population()

config.ini

@@ -4,12 +4,12 @@ movement = robot
 #accept: human, robot
 
 [CONSTANT]
-NumberOfBananas = 5
-NumberOfEarrings = 3
+NumberOfBananas = 15
+NumberOfEarrings = 0
 NumberOfPlants = 5
 BananaFilling = 25
-RobotStartPosition = 1, 1
-DockStationStartPosition = 9, 8
+RobotStartPosition = 5, 5
+DockStationStartPosition = 5, 6
 #9,8
 
 [NEURAL_NETWORK]
@@ -21,6 +21,6 @@ mode = full_clean
 
 [GENETIC_ALGORITHM]
 PopulationSize = 20
-DescendantsNumber = 5
-MutationProbability = 0.1
-IterationNumber = 100
+DescendantsNumber = 6
+MutationProbability = 0.3
+IterationNumber = 1_000

domain/commands/vacuum_move_command.py

@@ -27,6 +27,7 @@ class VacuumMoveCommand(Command):
                 if self.vacuum.get_container_filling() < 100:
                     self.vacuum.increase_container_filling()
                     self.world.delete_entities_at_Of_type(item.x, item.y, item.type)
+                    self.world.dust[end_x][end_y].remove(item)
 
         if self.world.is_docking_station_at(end_x, end_y):
             self.vacuum.dump_trash()

main.py

@@ -70,11 +70,7 @@ class Main:
             genetic_searcher = GeneticAlgorytm(self.world)
             genetic_searcher.run()
 
-            print(
-                str(genetic_searcher.best_path.walk)
-                + ": "
-                + str(genetic_searcher.best_distance)
-            )
+            genetic_searcher.print_top()
 
             robot_actions = genetic_searcher.best_path.real_path
 
@@ -190,9 +186,15 @@ def generate_world(tiles_x: int, tiles_y: int) -> World:
         world.add_entity(Entity(9, 3, "PLANT3"))
 
         numberOfEarrings = config.getint("CONSTANT", "NumberOfEarrings")
-        world.add_entity(Earring(9, 7))
-        world.add_entity(Earring(5, 5))
-        world.add_entity(Earring(4, 6))
+        for _ in range(numberOfEarrings):
+            temp_x = randint(0, tiles_x - 1)
+            temp_y = randint(0, tiles_y - 1)
+
+            while world.is_entity_at(temp_x, temp_y):
+                temp_x = randint(0, tiles_x - 1)
+                temp_y = randint(0, tiles_y - 1)
+
+            world.add_entity(Earring(temp_x, temp_y))
 
         for _ in range(config.getint("CONSTANT", "NumberOfBananas")):
             temp_x = randint(0, tiles_x - 1)
@@ -248,7 +250,7 @@ def generate_world(tiles_x: int, tiles_y: int) -> World:
             if world.garbage_at(x, y):
                 world.costs[x][y] = 1
             else:
-                world.costs[x][y] = 10
+                world.costs[x][y] = 2
     return world