Genetic algorithms explained in 6 minutes (...and 28 seconds)

00:06:27
https://www.youtube.com/watch?v=-kpcAa-qKwY

Summary

TLDRThis lesson covers genetic algorithms, a method for optimizing machine learning tasks. It uses the example of a character named Jonathan navigating a park to illustrate the steps involved in genetic algorithms: creating an initial population, defining a fitness function, selecting the fittest individuals, and applying crossover and mutation to generate new solutions. The importance of mutation is emphasized to prevent stagnation in local minima. The lesson concludes with a promise to teach programming genetic algorithms in Python in the next video.

Takeaways

  • 🧬 Genetic algorithms optimize solutions using natural selection principles.
  • 🐱 Jonathan represents a character navigating a park using genetic algorithms.
  • 🔍 The process starts with creating an initial population of solutions.
  • 📏 A fitness function measures how well a solution meets the goals.
  • 👥 Fittest individuals are selected to create the next generation.
  • 🔄 Crossover combines genes from parent solutions to create offspring.
  • ⚙️ Mutation introduces variability to explore new solutions.
  • 📉 High mutation rates can lead to excessive exploration.
  • 📈 Low mutation rates may cause slow adaptation to new solutions.
  • 💻 Next, learn to program genetic algorithms in Python.

Timeline

  • 00:00:00 - 00:06:27

    The lesson introduces genetic algorithms as an efficient method for optimizing machine learning tasks. It uses the analogy of a cat navigating a maze to explain how random guessing can eventually lead to a solution, albeit slowly. Genetic algorithms improve this process by employing a 'survival of the fittest' approach, which involves creating an initial population, defining a fitness function, selecting the fittest individuals, and using crossover and mutation to generate new solutions. The example of Jonathan, a character with four possible actions, illustrates how to create a DNA strand of actions and how to apply genetic algorithms to enhance his ability to navigate a park without obstacles.

Mind Map

Video Q&A

  • What are genetic algorithms?

    Genetic algorithms are optimization techniques inspired by the process of natural selection, used to solve complex problems more efficiently.

  • How do genetic algorithms work?

    They work by creating an initial population of solutions, evaluating their fitness, selecting the best ones, and using crossover and mutation to generate new solutions.

  • What is a fitness function?

    A fitness function is a measure used to evaluate how close a given solution is to achieving the set goals.

  • Why is mutation important in genetic algorithms?

    Mutation introduces variability, allowing the algorithm to explore new solutions and avoid getting stuck in local minima.

  • What is the role of crossover in genetic algorithms?

    Crossover combines the genetic information of two parent solutions to create offspring solutions.

  • How is the initial population created?

    The initial population is created by randomly selecting solutions from a set of possible actions or genes.

  • What happens if the mutation rate is too high?

    If the mutation rate is too high, the algorithm may explore too much and not effectively utilize the best solutions.

  • What happens if the mutation rate is too low?

    If the mutation rate is too low, the algorithm may converge too quickly and miss out on better solutions.

  • What is the next step after learning about genetic algorithms?

    The next step is to learn how to program genetic algorithms using Python.

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  • 00:00:00
    today's lesson is all about genetic
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    algorithms which are a really cool way
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    to optimize learning in machine learning
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    tasks if you're not quite sure what this
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    means
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    imagine you have a problem which if you
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    just kept some guessing random solutions
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    for you would eventually stumble across
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    one which works maybe the problem is a
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    big maze say you put a computer cat at
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    the beginning and you tell it you can
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    take up to 100 steps and each step you
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    can go in any direction you like find
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    the box if the cat gets completely
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    random routes for all of eternity or
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    maybe just like a few hours it would
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    eventually stumble across a correct
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    solution which got it from the beginning
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    of the maze to the box but this would
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    take a whole lot of time what genetic
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    algorithms do is use a survival of the
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    fittest type algorithm to reach solution
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    much faster the steps to creating a
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    genetic algorithm include creating an
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    initial population defining a fitness
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    function selecting the fittest members
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    of the population using crossover and
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    mutation to create the next generation
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    and repeating steps three to four until
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    we're able to solve our problem let's
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    jump into an example to learn what all
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    these steps mean and how to implement
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    them this is Jonathan Jonathan can take
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    four actions he can go left he can go
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    right you can go up and levitates
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    yeah Jonathan's pretty rad or you can go
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    down let's see how we can string these
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    controls together to create something
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    called a DNA strand
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    we'll give Jonathan a string of 14
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    actions to take each one randomly
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    selected each action can be called a
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    gene and when the genes are combined in
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    this way the entire strand is called the
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    DNA now let's drop Jonathan in a park
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    Jonathan needs to walk through the park
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    to get to work but if he slips on a
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    banana skin or crashes into a bird he'll
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    fall and not be able to carry on let's
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    see what our Jonathan does with his
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    randomly created DNA not the best now
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    you or I could look at this scene I
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    manually created Jonathan with a better
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    DNA who could easily travel much further
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    unscathed
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    but what if I can't be bothered to tell
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    Jonathan what to do I mean why can't he
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    do all the work while I just watch
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    Netflix or what if we were faced with a
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    course which was much more difficult for
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    you or I to manually figure out
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    let's devise a genetic algorithm to
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    produce the competence Jonathan step 1
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    create an initial population let's
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    choose a population of size 5 and give
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    them each a different DNA created by
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    randomly selecting the genes from the 4
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    action options
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    this is where each of our Jonathan's end
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    up step to define a fitness function
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    since we want Jonathan to end up exiting
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    the park let's say the fitness function
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    is calculated by counting the number of
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    steps between Jonathan and his initial
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    position
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    step three select the fittest members of
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    the population since our population size
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    is very small we'll only take the top to
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    Jonathan's but you could choose to
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    select more if your population was
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    larger step 4 cross over a mutation in
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    order to create better future Jonathan's
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    we're going to take our fittest members
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    and breed them to produce child
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    Jonathan's so how do we do that we take
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    the DNA strands from the two parent
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    Jonathan's we then need to decide how
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    we're going to combine them you can do
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    this in any manner you can think of as
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    long as you're taking information from
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    both parents to create a child DNA I'm
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    going to choose to split the DNA strands
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    into three and then randomly choose
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    which Jonathan will pass on their strand
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    of genes from each section let's create
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    a new generation of five Jonathan's
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    [Music]
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    great but we're not quite finished with
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    the chopped Jonathan's we still need to
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    mutate some of their genes
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    why because otherwise we can get stuck
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    in local minima imagine if by chance our
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    initial population of Jonathan's only
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    consisted of left writes and down
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    actions
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    then the best Jonathan could ever do
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    would only be to make it to the first
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    banana which is only two steps away from
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    the starting point Jonathan would never
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    make it out of the park because the
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    parents didn't have a jumping gene so
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    the children would never learn to jump
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    that's why we need a mutation so that
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    Jonathan can have the opportunity to try
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    new things if we make our mutation rate
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    too high Jonathan will explore a lot and
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    won't listen to his parents much so it
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    will take him a long time to be
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    competent if we make our mutation rate
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    too small
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    Jonathan will listen very well to both
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    his parents so he'll learn fast but it
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    will take him a long time to change his
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    ways if he's near the local minima a
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    good mutation rate is fairly small for
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    this problem let's choose a mutation
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    rates of not 0.1 this means every gene
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    and the DNA strand has a 10% chance of
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    being replaced with a random genes
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    let's meet eights all of the child's
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    Jonathan genes
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    [Music]
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    now our first child generation is ready
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    for action and we can pop them into the
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    park all we need to do now is repeat
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    steps three to four until we find a
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    Jonathan whose DNA allows them to
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    finally exit the park and go to work
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    hopefully you've enjoyed this video and
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    you now understand how genetic
  • 00:06:18
    algorithms function stick around for the
  • 00:06:20
    next video to learn how to actually
  • 00:06:22
    program genetic algorithms within Python
Tags
  • genetic algorithms
  • machine learning
  • optimization
  • fitness function
  • mutation
  • crossover
  • population
  • local minima
  • Python programming
  • algorithm design