Genetic algorithm to find best pathway
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18
board.py
18
board.py
@ -68,8 +68,10 @@ class Board:
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self.first_ground = self.board[0].index('T')
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self.show_board()
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self.init_board()
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self.bombfield = pathfinder.find_bomb(self.fields, self.first_ground)
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print(self.bombfield)
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#self.bombfield = pathfinder.find_bomb(self.fields, self.first_ground)
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self.genetic = pathfinder.Genetic(self.fields, self.first_ground)
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self.bombfield = self.genetic.genetic_path.fields_order[0]
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#print(self.bombfield)
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self.pathf = pathfinder.Pathfind(self.fields, self.bombfield, self.first_ground)
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self.ir = ImageRecognizer()
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self.dt = DecisionTree()
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@ -167,10 +169,14 @@ class Board:
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self.fields[agent.pos_field].object.move_away(SQUARE_WIDTH, SQUARE_HEIGHT)
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self.fields[agent.pos_field].redisp = True
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self.bombs -= 1
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if self.bombs > 0:
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self.bombfield = pathfinder.find_bomb(self.fields, agent.pos_field)
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#print(self.bombfield)
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self.pathf = pathfinder.Pathfind(self.fields, self.bombfield, agent.pos_field)
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if self.bombs > 0 and agent.pos_field == self.bombfield:
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#self.bombfield = pathfinder.find_bomb(self.fields, agent.pos_field)
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#print(self.bombfield)
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self.genetic.genetic_path.fields_order.remove(self.genetic.genetic_path.fields_order[0])
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self.bombfield = self.genetic.genetic_path.fields_order[0]
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print("next bombfield is: ", self.bombfield)
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self.pathf = pathfinder.Pathfind(self.fields, self.bombfield, agent.pos_field)
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def automove_agent(self,agent):
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@ -4,8 +4,8 @@ from keras import layers as ls
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import numpy as np
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image_height = 64
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image_width = 64
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image_height = 256
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image_width = 256
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batch_size = 32
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sets_path = "./learning_sets"
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model_path="./IR.model"
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@ -16,7 +16,7 @@ class ImageRecognizer():
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if train_new_model == True:
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# deklarowanie setów treningowych i testowych
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self.trainset = keras.utils.image_dataset_from_directory(sets_path, validation_split=0.2, subset="training",seed=0, image_size=(image_height, image_width),batch_size=batch_size)
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self.testset = keras.utils.image_dataset_from_directory(sets_path, validation_split=0.2,subset="validation", seed=0,image_size=(image_height, image_width),atch_size=batch_size)
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self.testset = keras.utils.image_dataset_from_directory(sets_path, validation_split=0.2,subset="validation", seed=0,image_size=(image_height, image_width),batch_size=batch_size)
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self.class_names = self.trainset.class_names
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self.trainset = self.trainset.cache().shuffle(1000).prefetch(buffer_size=tensorflow.data.AUTOTUNE)
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self.testset = self.testset.cache().prefetch(buffer_size=tensorflow.data.AUTOTUNE)
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144
pathfinder.py
144
pathfinder.py
@ -1,4 +1,5 @@
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import board
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import random
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def distance_between(startpoint, endpoint):
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@ -22,6 +23,149 @@ def find_bomb(fields, start_position):
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return bombfield
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class Chromosome:
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def __init__(self, fo, wl):
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self.fields_order = fo
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self.way_lenght = wl
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class Genetic:
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def __init__(self, fields, ap):
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self.bombfields = self.get_bombs(fields)
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self.agent_position = ap
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self.local_minimum_path = self.find_local_minimum_path()
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self.make_parents()
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self.parents = self.make_parents()
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self.children = self.make_children(self.parents)
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self.genetic_path = self.parents[0]
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for i in range(10000):
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self.genetic_path = self.find_genetic_path()
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print("Genetic path way: ", self.genetic_path.fields_order)
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print("Genetic path way lenght: ", self.genetic_path.way_lenght)
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print("Local minimum path: ", self.local_minimum_path.fields_order)
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print("Local minimum path way lenght: ", self.local_minimum_path.way_lenght)
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if self.local_minimum_path.way_lenght >= self.genetic_path.way_lenght:
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print("Wygrywa genetic path !!!")
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else:
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print("Local minimum path teoretycznie byłby lepszy :/")
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def get_bombs(self,fields):
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bombfields = []
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for i in range(len(fields)):
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if fields[i].type == 'B':
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bombfields.append(i)
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#print(bombfields)
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return bombfields
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def make_parents(self):
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parent_chromosomes = []
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for i in range(len(self.bombfields)*10):
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bombfieldscopy = self.bombfields.copy()
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single_chromosome = []
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weight_of_chromosome = 0
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for j in range(len(self.bombfields)):
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random_choice = random.randint(0,(len(bombfieldscopy)-1))
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single_chromosome.append(bombfieldscopy[random_choice])
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bombfieldscopy.remove(bombfieldscopy[random_choice])
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if j == 0:
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weight_of_chromosome = weight_of_chromosome + distance_between(self.agent_position,single_chromosome[j])
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else:
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weight_of_chromosome = weight_of_chromosome + distance_between(single_chromosome[j-1],single_chromosome[j])
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parent_chromosomes.append(Chromosome(single_chromosome,weight_of_chromosome))
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#print("parent chromosome ", i, ":")
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#print(parent_chromosomes[i].fields_order)
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#print(parent_chromosomes[i].way_lenght)
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return parent_chromosomes
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def make_children(self,parents):
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children_chromosomes = []
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parentscopy = parents.copy()
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for i in range(len(self.bombfields)*10):
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parentscopy = parents.copy()
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rch = random.randint(0,(len(self.bombfields)*10)-1)
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parent1 = parentscopy[rch].fields_order.copy()
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#print("parent 1 order: ", parent1)
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parentscopy.remove(parentscopy[rch])
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rch2 = random.randint(0,(len(self.bombfields)*10)-2)
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parent2 = parentscopy[rch2].fields_order.copy()
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#print("parent 2 order: ",parent2)
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parentscopy.remove(parentscopy[rch2])
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how_many_elements = random.randint(1,len(self.bombfields)-1)
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elements_to_switch = []
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single_chromosome = []
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weight_of_chromosome = 0
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for j in range(how_many_elements):
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random_choice = random.randint(0,(len(parent1)-1))
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elements_to_switch.append(parent1[random_choice])
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#print("element to switch: ", elements_to_switch[j])
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parent1.remove(elements_to_switch[j])
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#print("parent 2 order: ", parent2.fields_order)
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parent2.remove(elements_to_switch[j])
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parent2.append(elements_to_switch[j])
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single_chromosome = parent2.copy()
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for j in range(len(single_chromosome)):
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if j == 0:
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weight_of_chromosome = weight_of_chromosome + distance_between(self.agent_position,single_chromosome[j])
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else:
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weight_of_chromosome = weight_of_chromosome + distance_between(single_chromosome[j - 1],single_chromosome[j])
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children_chromosomes.append(Chromosome(single_chromosome,weight_of_chromosome))
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#print("children chromosome ", i, ":")
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#print(children_chromosomes[i].fields_order)
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#print(children_chromosomes[i].way_lenght)
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return children_chromosomes
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def find_genetic_path(self):
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chromosomes = []
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new_parents = []
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for i in range(len(self.parents)):
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chromosomes.append(self.parents[i])
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for i in range(len(self.children)):
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chromosomes.append(self.children[i])
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chromosomes.sort(key=lambda x: x.way_lenght)
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for j in range(len(self.bombfields)**2):
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new_parents.append(chromosomes[j])
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self.parents = new_parents
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self.children = self.make_children(self.parents)
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return chromosomes[0]
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def find_local_minimum_path(self):
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path = []
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bombfiledscopy = self.bombfields.copy()
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path_lenght = 0
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bombfield = -1
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mindist = board.BOARDCOLS ** 2
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for field in bombfiledscopy:
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dist = distance_between(self.agent_position,field)
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if dist < mindist:
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bombfield = field
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mindist = dist
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path_lenght = path_lenght + mindist
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path.append(bombfield)
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bombfiledscopy.remove(bombfield)
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while len(bombfiledscopy) > 0:
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mindist = board.BOARDCOLS ** 2
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for field in bombfiledscopy:
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dist = distance_between(bombfield, field)
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if dist < mindist:
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bombfield = field
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mindist = dist
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path_lenght = path_lenght + mindist
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path.append(bombfield)
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bombfiledscopy.remove(bombfield)
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return Chromosome(path,path_lenght)
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class Node:
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def __init__(self,fieldN,parent,destination,obstacle_deley_value):
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