genetic_algorithm.py

# -*- coding: utf-8 -*-
"""genetic_algorithm.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1gRTzndHDYAqsfTOmDmadcYMLtPZRJnYH
"""

#GENETIC ALGORITHM
import random

# Genetic Algorithm parameters
target = "1101010100110010"  # The target binary string we want to evolve towards
population_size = 50
mutation_rate = 0.1
generations = 100

# Create an initial population of random binary strings
def create_initial_population(size):
    return [''.join(random.choice('01') for _ in range(len(target))) for _ in range(size)]

# Evaluate the fitness of each individual in the population
def calculate_fitness(individual):
    return sum(1 for a, b in zip(individual, target) if a == b)

# Select parents for the next generation using roulette wheel selection
def select_parents(population):
    total_fitness = sum(calculate_fitness(individual) for individual in population)
    probabilities = [calculate_fitness(individual) / total_fitness for individual in population]
    return random.choices(population, probabilities, k=2)

# Perform single-point crossover between parents to produce children
def crossover(parent1, parent2):
    crossover_point = random.randint(1, len(target) - 1)
    child1 = parent1[:crossover_point] + parent2[crossover_point:]
    child2 = parent2[:crossover_point] + parent1[crossover_point:]
    return child1, child2

# Perform mutation on an individual with a certain probability
def mutate(individual):
    mutated = ''.join(
        c if random.random() > mutation_rate else random.choice('01')
        for c in individual
    )
    return mutated

# Main genetic algorithm loop
def genetic_algorithm():
    population = create_initial_population(population_size)

    for generation in range(generations):
        population = sorted(population, key=calculate_fitness, reverse=True)
        print(f"Generation {generation}: Best fitness = {calculate_fitness(population[0])}, Best individual = {population[0]}")

        new_population = []
        while len(new_population) < population_size:
            parent1, parent2 = select_parents(population)
            child1, child2 = crossover(parent1, parent2)
            child1 = mutate(child1)
            child2 = mutate(child2)
            new_population.extend([child1, child2])

        population = new_population[:population_size]

# Run the genetic algorithm
genetic_algorithm()