Answers.Body: <p>What you have basically described is NeuroEvolution of Augmenting Topologies (NEAT).  It has yet to achieve general intelligence.  Although it has been used many times to tackle specific problems.</p>
  <p>Evolutionary algorithms are, as of yet, fairly sample inefficient.  So while it may be possible to evolve intelligence this way, it may not happen on a reasonable timescale.</p>
  
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Answers.Text: Rather than selecting a neural network based on a fitness function, survival of the fittest occurs among logics within a neural network.
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Answers.Text: Another big drawback of evolutionary algorithms is their computational inefficiency. In every generation, I'd need a population of hundreds or thousands of models interact with each other and their environment some time to find the fittest individuals.
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Answers.Text: @Displayname if you look up Neuroevolution on Wikipedia you’ll get a history of the field back to 1994.   Weights, structure and parameters have all been tried over the years.  Not that AGI won’t come from this, just that it hasn’t yet.
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Body: <ol>
  <li>Periodically mutate the neural network</li>
  <li>Random logics are created on the neural network</li>
  <li>Survival of the fittest occurs as random logics interact with each other</li>
  <li>Evolution occurs</li>
  <li>Evolved organisms have general intelligence to respond to random logics</li>
  </ol>
  
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Comments.Text: A big part of the question whether or not this kind of learning could yield any sort of intelligence is the fitness function. Besides, what do you mean by "logics"?
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Tags: <evolutionary-algorithms>
Title: Could this method create general intelligence?
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ThreadText: # Could this method create general intelligence?
  
  (Asked by: username_0 on 2024-10-15)
  
  1. Periodically mutate the neural network
  2. Random logics are created on the neural network
  3. Survival of the fittest occurs as random logics interact with each other
  4. Evolution occurs
  5. Evolved organisms have general intelligence to respond to random logics
  
  **Tags:** evolutionary-algorithms
  
  > **Comments:**
  > **username_1**: A big part of the question whether or not this kind of learning could yield any sort of intelligence is the fitness function. Besides, what do you mean by "logics"?  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2024-10-15. Score: 2
  
  What you have basically described is NeuroEvolution of Augmenting Topologies (NEAT). It has yet to achieve general intelligence. Although it has been used many times to tackle specific problems.
  
  Evolutionary algorithms are, as of yet, fairly sample inefficient. So while it may be possible to evolve intelligence this way, it may not happen on a reasonable timescale.
  
  > **Comments:**
  > **username_0**: Rather than selecting a neural network based on a fitness function, survival of the fittest occurs among logics within a neural network.  
  > **username_1**: Another big drawback of evolutionary algorithms is their computational inefficiency. In every generation, I'd need a population of hundreds or thousands of models interact with each other and their environment some time to find the fittest individuals.  
  > **username_2**: @username_0 if you look up Neuroevolution on Wikipedia you’ll get a history of the field back to 1994.   Weights, structure and parameters have all been tried over the years.  Not that AGI won’t come from this, just that it hasn’t yet.  
  
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