Why are there 3 instead of 10 objects in this GA chromosome?

Question

I'm building a genetic algorithm in Python that takes Location input from a .csv file (id, name, x, y), puts them in a list and calculates the optimal distances.

At the end, the program is supposed to return the best chromosome, and I want to print the location names in list form. The issue is that in this final chromosome (final_location_list in main.py), there are only 3 locations instead of the expected 10. Why is this so?

Chromosome.py

import random
import math
import csv
from typing import List

class Location:
    def __init__(self, id, name, x_coord, y_coord):
        self.id = int(id)
        self.name = name
        self.x_coord = float(x_coord)
        self.y_coord = float(y_coord)

# extract data from dataset and put in a List with Location values
dataset = []

with open('test.csv', 'r') as file:
    reader = csv.reader(file, delimiter=',')
    next(reader)
    for row in reader:
        id, name, x, y = row[0], row[2], row[6], row[7]
        dataset.append(Location(id=id, name=name, x_coord=x, y_coord=y))

# take dataset as parameter and create distance matrix

def generate_dist_matrix(nodes_list):
    node_number = len(nodes_list)
    matrix = [[0 for _ in range(node_number)] for _ in range(node_number)]

    for i in range(node_number):
        for j in range(node_number):
            if i != j:
                x1, y1 = nodes_list[i].x_coord, nodes_list[i].y_coord
                x2, y2 = nodes_list[j].x_coord, nodes_list[j].y_coord
                matrix[i][j] = math.dist((x1, y1), (x2, y2))

    return matrix

dist_matrix = generate_dist_matrix(dataset)

# chromosome class contains name, x, y, distance, and fitness.
# Use distance to calculate fitness.

class Chromosome:
    def __init__(self, nodes_list):
        self.chromosome = nodes_list
        self.ids_in_list = [node.id for node in nodes_list]
        self.cost = self.calculate_distance()
        self.fitness_value = 1 / self.cost if self.cost != 0 else float('inf')

    def calculate_distance(self):
        total_distance = 0
        for i in range(len(self.chromosome) - 1):
            total_distance += dist_matrix[self.chromosome[i].id - 1][self.chromosome[i + 1].id - 1]
        total_distance += dist_matrix[self.chromosome[-1].id - 1][self.chromosome[0].id - 1]  # Return to the starting point
        return total_distance

main.py

import GeneticAlgorithm as GA
import Chromosome as chr  # Ensure these modules are implemented correctly

# Example usage
generations = 100
pop_size = 10
mut_rate = 0.2
dataset = chr.dataset  # Assuming chr.dataset contains the dataset

def execute_GA(generations, pop_size, mut_rate, dataset):
    # Initialize the population
    generated_pop = GA.initialize(dataset, pop_size)

    costs_for_plot = []

    # Execute the GA for the specified number of generations
    for i in range(generations):
        # Create a new generation
        new_gen = GA.new_generation(generated_pop, mut_rate)

        # Print the best cost for the current generation
        best_cost = GA.find_best(new_gen).cost
        if (best_cost == 0):
            break
        print(f"Generation {i + 1}: Best Cost = {best_cost}")
        # Store the best cost for plotting
        costs_for_plot.append(best_cost)

        # Update the population for the next generation
        generated_pop = new_gen

    # Return the final generation and costs for plotting
    return new_gen, costs_for_plot

final_generation, costs_for_plot = execute_GA(generations, pop_size, mut_rate, dataset)

best_solution = GA.find_best(final_generation)

print(best_solution.chromosome)

final_location_list = []

for i in range(len(best_solution.chromosome)):
    # get id from each location and print out its associated name from dataset

    location_id = dataset[i].id
    location_name = dataset[location_id-1].name
    final_location_list.append(location_name)

print(final_location_list)

GeneticAlgorithm.py

import random
import Chromosome as chromo

def create_random_list(node_list):
    start = node_list[0]
    temp = node_list[1:]
    random.shuffle(temp)
    temp.insert(0, start)
    temp.append(start)
    return temp

def initialize(data, pop_size): #generates an initial population
    initial_pop = []
    for _ in range(pop_size):  # Corrected iterating over pop_size
        temp = create_random_list(data)
        generated_chromosome = chromo.Chromosome(temp)
        initial_pop.append(generated_chromosome)
    return initial_pop

def selection(population): #selects two chromosomes randomly from the population
    ch1 = random.choice(population)
    ch2 = random.choice(population)
    if ch1.fitness_value > ch2.fitness_value:
        winner = ch1
    else:
        winner = ch2

    return winner
    #while ch2 == ch1:
    #    ch2 = random.choice(population)
    #return ch1, ch2

def crossover(ch1, ch2):
    indexes = random.randint(2, 5)
    crossed_ch1 = ch1.chromosome[1:indexes] + [item for item in ch2.chromosome[1:-1] if item not in ch1.chromosome]
    crossed_ch2 = ch2.chromosome[1:indexes] + [item for item in ch1.chromosome[1:-1] if item not in ch2.chromosome]
    crossed_ch1.insert(0, ch1.chromosome[0])
    crossed_ch1.append(ch1.chromosome[0])
    crossed_ch2.insert(0, ch2.chromosome[0])
    crossed_ch2.append(ch2.chromosome[0])
    return crossed_ch1, crossed_ch2

def mutation(chromosome):
    swap_index1 = random.randint(1, len(chromosome) - 2)  # Avoid swapping start/end nodes
    swap_index2 = random.randint(1, len(chromosome) - 2)
    chromosome[swap_index1], chromosome[swap_index2] = chromosome[swap_index2], chromosome[swap_index1]
    return chromosome

def find_best(generation):
    return min(generation, key=lambda x: x.cost)

def new_generation(previous_gen, mutation_rate):
    new_generation = [find_best(previous_gen)]
    for _ in range(len(previous_gen) // 2):
        parent_1 = selection(previous_gen)
        parent_2 = selection(previous_gen)
        child_1, child_2 = crossover(parent_1, parent_2)
        if random.random() < mutation_rate:
            child_2 = mutation(child_2)
        new_generation.append(chromo.Chromosome(child_1))
        new_generation.append(chromo.Chromosome(child_2))

    return new_generation

test.csv

id,place_id,name,main_category,reviews,rating,latitude,longitude
1,ChIJvSXoM47DSjARJx_AFWVTwcU,Penang Little India,Tourist attraction,15127,4.3,5.417400799,100.3393882
2,ChIJiefCZo_DSjARmpsvzxP60eY,Padang Kota Lama (Esplanade),Tourist attraction,14240,4.3,5.420548751,100.3443193
3,ChIJ0ZhzIZHDSjARWcrnjUdMN-M,Georgetown UNESCO Historic Site,History,13056,4.4,5.417185276,100.3380258
4,ChIJQY55_XrCSjARE4XPYsmgG54,Penang Hill,Nature,12895,4.3,5.409486582,100.2775463
5,ChIJZYb8XRjCSjARxD5Df-qEkCw,Kek Lok Si Temple,Religious site,9974,4.4,5.399854925,100.2736609
6,ChIJ-cCMa5LDSjARF1sg9FiPauc,Penang Street Art,Tourist attraction,8892,4.4,5.414788457,100.3382397
7,ChIJ83gcpVjoSjARH4vjVeRjzHg,Entopia by Penang Butterfly Farm,Nature,8122,4.4,5.447856468,100.215064
8,ChIJWeDiIPHCSjAROzyNNz_QUx8,Penang Botanic Gardens,Nature,7337,4.5,5.437942953,100.2906788
9,ChIJ50W1D43DSjARlPqYV1MqscE,Chew Jetty,Tourist attraction,6493,4.1,5.412995206,100.3398099
10,ChIJkbOK8JbDSjARukDZqTnwoRo,Penang Road Famous Teochew Chendul,Food,6479,4.1,5.417153557,100.3309071

Thank you.