Comment by esafak

Comment by esafak 2 days ago

Where does the genome, genetic representation, you are evolving come from? The same raw features you use in neural networks? Then you optimize using that? If so, why not use gradient descent, which is faster? And this is still a step behind neural networks even apart from the optimization method, because neural networks use composition to learn features. How are you doing that?

Do you have any real world examples of your method that are competitive with DL methods?

bob1029 2 days ago

> Where does the genome, genetic representation, you are evolving come from

The instruction set of the program that is being searched for.

This is probably the best publicly available summary of the idea I am pursuing:

https://github.com/kurtjd/brainfuck-evolved

Reply View 11 replies

esafak 2 days ago

A program is composed of arbitrarily many instructions of your set. How are you accounting for this; trying every possible program length? And you are considering the simpler case where the search space is discrete, unlike the continuous spaces in most machine learning problems.
I think you need to think this through some more. You may see there is a reason nobody uses genetic algorithms for real world tasks.

Reply View | 7 replies
- bob1029 2 days ago
  
  > How are you accounting for this; trying every possible program length?
  Part of the mutation function involves probabilistically growing and shrinking the program size (i.e., inserting and removing random instructions).
  > And you are considering the simpler case where the search space is discrete, unlike the continuous spaces in most machine learning problems.
  All "continuous spaces" that embody modern machine learning techniques are ultimately discrete.
  
  Reply View | 6 replies
  
  esafak 2 days ago
  
  No, they are not. Model outputs can be discretized but the model parameters (excluding hyperparameters) are typically continuous. That's why we can use gradient descent.
  
  Reply View | 5 replies
dartos 2 days ago

you're talking about specifically using genetic programming to create new programs as opposed to gradient decend in LLMs to minimize a loss function, right?
How would you construct a genetic algorithm to produce natural language like LLMs do?
Forgive me if i'm misunderstanding, but in programming we have "tokens" which are minimal meaningful bits of code.
For natural languages it's harder. "Words" are not super meaningful on their own, i don't think. (at least not as much as a token) so how would you break down natural language for a genetic algorithm?

Reply View | 2 replies
- bob1029 2 days ago
  
  > how would you break down natural language for a genetic algorithm?
  The entire point is that you do not bother trying. From an information theory and computational perspective, raw UTF-8 bytes can work just as well as "tokens".
  The program that is being evolved is expected to develop whatever strategy is best suited to providing the desired input/output transformation. Back to the bitter lesson on this one.
  
  Reply View | 1 reply
  
  dartos 2 days ago
  
  I’ll need to read up on genetic algorithms, I think.
  That sounds really cool, but coming from training other statistical models, im having a hard time imagining what the training loop looks like.
  
  Reply View | 0 replies