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  2. Genetic algorithm

    Type of algorithm
    OverviewApplicationsAdvantages
  3. 1

    A Genetic Algorithm (GA) is an optimization technique inspired by the principles of natural selection and genetics. It is widely used in Artificial Intelligence (AI) to solve optimization and search problems. GAs simulate the process of "survival of the fittest," where better solutions evolve over successive generations.

    Key Concepts of Genetic Algorithms

    1. Population: A group of potential solutions (individuals) represented as chromosomes.

    2. Fitness Function: A measure to evaluate how good a solution is.

    3. Selection: Choosing the fittest individuals for reproduction.

    4. Crossover: Combining genes of two parents to create offspring.

    5. Mutation: Introducing random changes to maintain diversity and avoid local optima.

    Example: Generating a Target String

    The goal is to evolve a random string into a target string (e.g., "I love AI") using genetic algorithms.

    Python Implementation

    import random

    # Define the target string and gene pool
    TARGET = "I love AI"
    GENES = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ 1234567890"

    # Fitness function: Counts mismatched characters
    def calculate_fitness(chromosome):
    return sum(1 for expected, actual in zip(TARGET, chromosome) if expected != actual)

    # Generate a random chromosome
    def create_chromosome():
    return ''.join(random.choice(GENES) for _ in range(len(TARGET)))

    # Perform crossover between two parents
    def crossover(parent1, parent2):
    child = ""
    for p1, p2 in zip(parent1, parent2):
    child += p1 if random.random() < 0.5 else p2
    return child

    # Perform mutation on a chromosome
    def mutate(chromosome):
    index = random.randint(0, len(chromosome) - 1)
    mutated_gene = random.choice(GENES)
    return chromosome[:index] + mutated_gene + chromosome[index + 1:]

    # Genetic Algorithm
    def genetic_algorithm():
    population = [create_chromosome() for _ in range(100)]
    generation = 0

    while True:
    # Sort population by fitness
    population = sorted(population, key=calculate_fitness)
    best_individual = population[0]
    best_fitness = calculate_fitness(best_individual)

    print(f"Generation {generation}: {best_individual} (Fitness: {best_fitness})")

    # Stop if target is reached
    if best_fitness == 0:
    break

    # Select top 10% for reproduction
    top_individuals = population[:10]

    # Create new population through crossover and mutation
    new_population = []
    for _ in range(90):
    parent1, parent2 = random.sample(top_individuals, 2)
    child = crossover(parent1, parent2)
    if random.random() < 0.1: # Mutation probability
    child = mutate(child)
    new_population.append(child)

    # Add top individuals to maintain elitism
    population = top_individuals + new_population
    generation += 1

    # Run the algorithm
    genetic_algorithm()
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    What is genetic algorithm (GA) and how to use it?
    What is GA and how to use it to train Machine Learning models? Genetic Algorithm (GA) is a type of natural computing algorithm, which are algorithms developed to try to solve problems by replicating phenomena and behaviors present in nature.
    towardsdatascience.com
    Wikipedia
    https://en.wikipedia.org/wiki/Genetic_algorithm
    Genetic algorithm - Wikipedia
  1. GeeksForGeeks
    https://www.geeksforgeeks.org › dsa › genetic-algorithms

    Genetic Algorithms - GeeksforGeeks

    Feb 10, 2026 · A Genetic Algorithm (GA) is a population-based evolutionary optimization technique inspired by the principles of natural selection and genetics.

  2. Wikipedia
    https://en.wikipedia.org › wiki › Genetic_algorithm

    Genetic algorithm - Wikipedia

    An online interactive Genetic Algorithm tutorial for a reader to practise or learn how a GA works: Learn step by step or watch global convergence in batch, change the …

  3. The Knowledge Academy
    https://www.theknowledgeacademy.com › blog › genetic-alg…

    Genetic Algorithms in Machine Learning: All you need to …

    Mar 20, 2026 · Explore the fascinating role of Genetic Algorithms in Machine Learning. This thorough blog delves into their nature-inspired methodologies, …

  4. cornell.edu
    https://optimization.cbe.cornell.edu › index.php

    Genetic algorithm - Cornell University Computational …

    Dec 15, 2024 · The Genetic Algorithm (GA) is an optimization technique inspired by Charles Darwin's theory of evolution through natural selection [1]. First developed …

  5. DataCamp
    https://www.datacamp.com › tutorial › genetic-algorithm-pyt…

    Genetic Algorithm: Complete Guide With Python …

    Jul 29, 2024 · A genetic algorithm goes through a series of steps that mimic natural evolutionary processes to find optimal solutions. These steps allow the population …

  6. Applied Roots
    https://www.appliedaicourse.com › blog › genetic-algorithm …

    Genetic Algorithm in Machine Learning

    Jan 25, 2025 · Genetic Algorithms (GAs) have a broad range of applications in machine learning, where they enhance model performance, reduce complexity, and …

  7. Towards Data Science
    https://towardsdatascience.com › geneti

    Genetic Algorithm and its practicality in Machine Learning

    Dec 26, 2022 · What is GA and how to use it to train Machine Learning models? Genetic Algorithm (GA) is a type of natural computing algorithm, which are …

  8. The IoT Academy
    https://www.theiotacademy.co › blog › genetic-algorithms-in-machine-learning

    Genetic Algorithms in Machine Learning: A Complete Guide

    Mar 12, 2026 · A complete guide to genetic algorithms in machine learning. Understand selection, crossover, mutation, advantages, and how they solve complex optimization problems.

  9. ResearchGate
    https://www.researchgate.net › publication

    (PDF) Genetic Algorithm and Machine Learning

    Nov 18, 2022 · Genetic algorithm is based on the natural search process, which mimics natural growth and employs approaches inspired by natural evolution to …

  10. Medium
    https://medium.com › @bdacc_club

    GENETIC ALGORITHMS IN MACHINE LEARNING - Medium

    Feb 11, 2024 · Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired …

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