genetic_algorithm .ipynb

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     "text": [
      "Fittest chromosome: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
     ]
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   "source": [
    "import random\n",
    "\n",
    "# Define the genetic algorithm parameters\n",
    "population_size = 100\n",
    "chromosome_length = 10\n",
    "mutation_rate = 0.1\n",
    "generations = 100\n",
    "\n",
    "# Define the fitness function\n",
    "def fitness(chromosome):\n",
    "    # Calculate the fitness of the chromosome\n",
    "    fitness_value = 0\n",
    "    for gene in chromosome:\n",
    "        if gene == 1:\n",
    "            fitness_value += 1\n",
    "    return fitness_value\n",
    "\n",
    "# Define the crossover function\n",
    "def crossover(parent_1, parent_2):\n",
    "    # Choose a random crossover point\n",
    "    crossover_point = random.randint(0, chromosome_length - 1)\n",
    "    \n",
    "    # Create the offspring\n",
    "    offspring = parent_1[:crossover_point] + parent_2[crossover_point:]\n",
    "    \n",
    "    return offspring\n",
    "\n",
    "# Define the mutation function\n",
    "def mutate(chromosome):\n",
    "    # Mutate each gene in the chromosome\n",
    "    for i in range(chromosome_length):\n",
    "        if random.random() < mutation_rate:\n",
    "            chromosome[i] = 1 - chromosome[i]\n",
    "    \n",
    "    return chromosome\n",
    "\n",
    "# Create the initial population\n",
    "population = []\n",
    "for i in range(population_size):\n",
    "    chromosome = [random.randint(0, 1) for j in range(chromosome_length)]\n",
    "    population.append(chromosome)\n",
    "\n",
    "# Evolve the population for a number of generations\n",
    "for generation in range(generations):\n",
    "    # Evaluate the fitness of each chromosome in the population\n",
    "    fitness_values = [fitness(chromosome) for chromosome in population]\n",
    "    \n",
    "    # Select the parents for crossover\n",
    "    parent_1 = population[fitness_values.index(max(fitness_values))]\n",
    "    parent_2 = population[fitness_values.index(max(fitness_values))]\n",
    "    \n",
    "    # Create the new population through crossover and mutation\n",
    "    new_population = []\n",
    "    for i in range(population_size):\n",
    "        offspring = crossover(parent_1, parent_2)\n",
    "        offspring = mutate(offspring)\n",
    "        new_population.append(offspring)\n",
    "    \n",
    "    # Replace the old population with the new population\n",
    "    population = new_population\n",
    "\n",
    "# Print the fittest chromosome in the final population\n",
    "fitness_values = [fitness(chromosome) for chromosome in population]\n",
    "fittest_chromosome = population[fitness_values.index(max(fitness_values))]\n",
    "print(\"Fittest chromosome:\", fittest_chromosome)\n"
   ]
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