Answers.Body: <p>It is a simplified but ultimately correct description of evolutionary computation.</p>
  <p>Technically a large population is not needed.  (1+1)-ES makes a modification and decides if it is better or worse, replacing the one learner if it is.</p>
  <p>But broadly the strategies can be seen as gradient-free hill climb optimization and they can be quite effective learners.</p>
  
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Answers.Body: <p>Yes, that is how <a href="https://en.wikipedia.org/wiki/Evolutionary_algorithm" rel="nofollow noreferrer">evolutionary algorithms</a> work in general. They are very suitable for <a href="https://en.wikipedia.org/wiki/Hyperparameter_optimization" rel="nofollow noreferrer">hyperparameter optimization</a>, but I don't think they are much used for training the actual models. When the models start having millions or billions of parameters, they converge much faster by using <a href="https://en.wikipedia.org/wiki/Stochastic_gradient_descent" rel="nofollow noreferrer">gradient descend</a> algorithms.</p>
  <p>Also, the illustrated bot brains resemble more <a href="https://en.wikipedia.org/wiki/Decision_tree" rel="nofollow noreferrer">decision trees</a> than neural networks.</p>
  
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Answers.Text: What you say about sample efficiency is correct but it’s important to note that GAs work on non-differential problems where gradient descent cannot be used.  Further, decision trees are only one model which can be evolved.  NEAT evolves neural networks and others evolve rule lists, support vectors or almost every other model.
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Answers.Body: <p>Getting through <a href="https://dl.acm.org/doi/pdf/10.1145/3467477" rel="nofollow noreferrer">this paper</a> (1)  I feel that the definition you ask about describes well only a subfield of evolutionary machine learning (EML) and EML is a broader field than that.</p>
  <p>The authors of the paper I mentioned frame EML as a bit fuzzy term that is a bag for other subfields. I think that <strong>your definition suits Evolutionary Reinforcement Learning</strong>.</p>
  <p><a href="https://i.sstatic.net/kE7uK2qb.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/kE7uK2qb.png" alt="Evolionary Reinforcement Learning diagram from the paper" /></a></p>
  <p>(1) Telikani, A., Tahmassebi, A., Banzhaf, W., &amp; Gandomi, A. H. (2021). Evolutionary machine learning: A survey. ACM Computing Surveys (CSUR), 54(8), 1-35.</p>
  
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Answers.Text: Nothing in the original post describes RL.  It’s a simple classification problem.
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Answers.Text: @foreverska you might be right, I've been anchored with the 'bot' term. So, coming back to the paper, this case would be classified into a category of Evolutionary Neural Networks and Deep Learning.
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Body: <p>In this video : <a href="https://youtu.be/R9OHn5ZF4Uo?feature=shared" rel="nofollow noreferrer"><em>How AIs, like ChatGPT, Learn</em></a>, narrator describes a form of machine learning, specifically <strong>evolutionary algorithms</strong> or <strong>evolutionary machine learning</strong>.</p>
  <p>Stating that, Initially, the <em>builder</em> bot creates student bots almost randomly, and the <em>teacher</em> bot tests them using images of bees and threes. The <em>student</em> bots that survive the tests at first are just lucky.</p>
  <p>However, by combining these lucky bots, keeping only the best-performing ones, and making random variations, the process gradually evolves more capable bots. Over time, this leads to <em>a student bot that isn't merely lucky</em> but genuinely begins to distinguish bees from threes.</p>
  <p>This iterative process of <strong>testing</strong>, <strong>selecting</strong>, and <strong>modifying</strong> continues until a highly effective bot emerges, even though neither the builder, the teacher, nor humans understand how it works.</p>
  <p>How correct this description of evolutionary machine learning actually is?</p>
  
Tags: <machine-learning><image-recognition>
Title: Is this video’s description of evolutionary learning correct?
UserDisplayName: Gopal Kaushik
ThreadText: # Is this video’s description of evolutionary learning correct?
  
  (Asked by: username_0 on 2024-08-29)
  
  In this video : [*How AIs, like ChatGPT, Learn*](https://youtu.be/R9OHn5ZF4Uo?feature=shared), narrator describes a form of machine learning, specifically **evolutionary algorithms** or **evolutionary machine learning**.
  
  Stating that, Initially, the *builder* bot creates student bots almost randomly, and the *teacher* bot tests them using images of bees and threes. The *student* bots that survive the tests at first are just lucky.
  
  However, by combining these lucky bots, keeping only the best-performing ones, and making random variations, the process gradually evolves more capable bots. Over time, this leads to *a student bot that isn't merely lucky* but genuinely begins to distinguish bees from threes.
  
  This iterative process of **testing**, **selecting**, and **modifying** continues until a highly effective bot emerges, even though neither the builder, the teacher, nor humans understand how it works.
  
  How correct this description of evolutionary machine learning actually is?
  
  **Tags:** machine-learning, image-recognition
  
  
  ---
  
  ## 3 answers
  
  ### Answer by username_1 on 2024-08-29. Score: 1
  
  It is a simplified but ultimately correct description of evolutionary computation.
  
  Technically a large population is not needed. (1+1)-ES makes a modification and decides if it is better or worse, replacing the one learner if it is.
  
  But broadly the strategies can be seen as gradient-free hill climb optimization and they can be quite effective learners.
  
  
  ---
  
  ### Answer by username_2 on 2024-08-29. Score: 0
  
  Yes, that is how [evolutionary algorithms](https://en.wikipedia.org/wiki/Evolutionary_algorithm) work in general. They are very suitable for [hyperparameter optimization](https://en.wikipedia.org/wiki/Hyperparameter_optimization), but I don't think they are much used for training the actual models. When the models start having millions or billions of parameters, they converge much faster by using [gradient descend](https://en.wikipedia.org/wiki/Stochastic_gradient_descent) algorithms.
  
  Also, the illustrated bot brains resemble more [decision trees](https://en.wikipedia.org/wiki/Decision_tree) than neural networks.
  
  > **Comments:**
  > **username_1**: What you say about sample efficiency is correct but it’s important to note that GAs work on non-differential problems where gradient descent cannot be used.  Further, decision trees are only one model which can be evolved.  NEAT evolves neural networks and others evolve rule lists, support vectors or almost every other model.  
  
  ---
  
  ### Answer by username_3 on 2024-08-29. Score: -1
  
  Getting through [this paper](https://dl.acm.org/doi/pdf/10.1145/3467477) (1) I feel that the definition you ask about describes well only a subfield of evolutionary machine learning (EML) and EML is a broader field than that.
  
  The authors of the paper I mentioned frame EML as a bit fuzzy term that is a bag for other subfields. I think that **your definition suits Evolutionary Reinforcement Learning**.
  
  [![Evolionary Reinforcement Learning diagram from the paper](https://i.sstatic.net/kE7uK2qb.png)](https://i.sstatic.net/kE7uK2qb.png)
  
  (1) Telikani, A., Tahmassebi, A., Banzhaf, W., & Gandomi, A. H. (2021). Evolutionary machine learning: A survey. ACM Computing Surveys (CSUR), 54(8), 1-35.
  
  > **Comments:**
  > **username_1**: Nothing in the original post describes RL.  It’s a simple classification problem.  
  > **username_3**: @username_1 you might be right, I've been anchored with the 'bot' term. So, coming back to the paper, this case would be classified into a category of Evolutionary Neural Networks and Deep Learning.  
  
  ---
  
  