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Genetic algorithms yes but genetic programming is actually able to invent new things. Possibly optimizing the set of weight of a neural network to solve a given

Genetic algorithms yes but genetic programming is actually able to invent new things. Possibly optimizing the set of weight of a neural network to solve a given problem is more similar to genetic programming, if you see the NN as a computational unit.

andreyk 9y ago
Genetic algorithms 'invent new things' by doing exactly the same thing as other optimization methods - tweaking the values of particular parameters. The only difference in is how they do so.
- api 9y ago
  Isn't this kind of a banal statement? Doesn't everything that learns in any way whatsoever "tweak values of particular parameters"?
  - IanCal 9y ago
    It's banal but sensible in response to someone claiming a large difference between two fundamentally similar things. Either both or neither "invents".
    - dmix 9y ago
      As a non-expert in the field my impression from reading the article wasn't that they were saying there was a large difference, or it's a whole new approach, only that is was a progression over DL. The entire article repeatedly relates everything in the context of what DL accomplishes and only introduces EC as an better optimized version. Which doesn't lead one to think they are significantly different approaches but variations or tweaked versions of the same general thing...
      - andreyk 9y ago
        The article makes this claim: "it addresses a distinctly different need: Whereas DL focuses on modeling what we already know, EC focuses on creating new knowledge." This is just wrong.
- antirez 9y ago
  Optimizing genes leading to an intelligent neural network is what biology did basically. If your optimization problem changes the behavior of something able to compute in order to maximize the ability to solve a task, then that's inventing things IMHO.
  - IanCal 9y ago
    Which is then no different to DL or any other learning algorithm.
  - pmalynin 9y ago
    Respectfully, We've had "learning-to-learn" algorithms for a few years now. Including LSTMs that can be learn the gradients for other LSTMs, and deep-RL algorithms that can optimize neural networks. Its hard to say its inventing new things... there is still a clear goal - a loss - and we are optimizing it; poorly in the case of genetic algorithms. I would say genetic methods are simply poorly described reinforcement learning problems, which means that there is a 1-to-1 mapping between Deep Learning and Genetic Algorithms. PS. Making a distinction between "Genetic Algorithms" and "Genetic Programming" is like calling Deep Learning "Differential Programming" -- changing the name of a thing does not change the thing itself.
    - antirez 9y ago
      I don't want to mean that this is "novel" at all, I played a lot with genetic programming like 20 years ago. However in my opinion genetic algorithms and genetic programming are not the same thing, while closely related: genetic programming is using genetic algorithms as a search strategy to find a way to write a program in order to solve a problem. It is more closely related to reinforcement learning in theory, you just have a problem, a fitness function, and you provide no hints about how to solve it. Note how this is fundamentally different than having already an algorithm in lack of good parameters, which is what GA does. When such parameters are computer instructions, things start to be semantically interesting. However there is a big difference between reinforcement learning and genetic programming: the second will output a program that can be simplified and understood. NNs are much more opaque so even when they outperform known techniques, what they do is not clear. A more practical example: 20 years ago I wrote a GP framework based on a simple stack language (so that programs are always valid, it's an alternative to use S-expressions). Then I used it in order to generate a new hashing function to minimize the collision I had in my hash table. The output of the GP was a code snippet that I could understand, translate into a specification, re-write in C. It effectively invented a good hashing function. You can see everything as an optimization problem, but at the end of the day if the output of a GP is similar to the output of a mathematician that you hired to write a better hash function, well, this is kinda questionable if it was just optimizing or if while doing it the program invented something. Also note that in the 90s things produced by genetic programming went patented, because they were novel algorithms.
- fnl 9y ago
  While I agree that the quotes you made sound more like marketing than science, antirez has a point here: They might be using GA to evolve a new activation function represented as a GP tree. But that's just my guess. And even so, I am not terribly convinced that would lead to astonishingly new activation functions that are better than the known ones.
- Eliezer 9y ago
  Gradient descent: Run the neural net, evaluate the fitness function, shift parameters in the direction of the derivative of the fitness function. Genetic algorithm: Shift parameters in a batch of random ways, evaluate using fitness function, keep the best. There is nothing more fundamentally 'creative' about one than the other; these are both just attacks on the problem "here is this function, find parameter settings that get higher scores in it".
  - eafan 9y ago
    An important difference is the manner by which 'exploration' is codified in evolutionary algorithms, which allows for these algorithms to have a tune-able mix of hill-climbing and niche-finding. RL-based systems, Evolutionary Strategies, Swarm Intelligence, ... all are essentially hill-climbing and are typically much better at the 'exploit' aspect than the 'explore' aspect of the search. When you pair evolutionary algorithms with novelty search, for instance, you get very good coverage, as these guys have done with their sorting network work.
- jacques_chester 9y ago
  Antirez distinguished between genetic algorithms and genetic programming. As you say, GAs evolve parameters to the fitness function. But GP does, in fact, evolve programs. They are very distinct schools of work that evolved independently. Some other major schools are Learning Classifier Systems, Evolution Strategies and Evolutionary Programming. The high-level name these days is Evolutionary Computation. In turn typically lumped under Nature-Inspired Computing, or sometimes Metaheuristics.
nickpsecurity 9y ago
To support your claim, here's plenty of examples of GP inventing new things: http://www.genetic-programming.org/combined.php http://www.genetic-programming.org/combined.php Here's a fun article from Popular Science on Koza and his "invention machine" that readers might enjoy: https://www.popsci.com/scitech/article/2006-04/john-koza-has-built-invention-machine https://www.popsci.com/scitech/article/2006-04/john-koza-has...

andreyk 9y ago

Genetic algorithms 'invent new things' by doing exactly the same thing as other optimization methods - tweaking the values of particular parameters. The only difference in is how they do so.

api 9y ago
Isn't this kind of a banal statement? Doesn't everything that learns in any way whatsoever "tweak values of particular parameters"?
- IanCal 9y ago
  It's banal but sensible in response to someone claiming a large difference between two fundamentally similar things. Either both or neither "invents".
  - dmix 9y ago
    As a non-expert in the field my impression from reading the article wasn't that they were saying there was a large difference, or it's a whole new approach, only that is was a progression over DL. The entire article repeatedly relates everything in the context of what DL accomplishes and only introduces EC as an better optimized version. Which doesn't lead one to think they are significantly different approaches but variations or tweaked versions of the same general thing...
    - andreyk 9y ago
      The article makes this claim: "it addresses a distinctly different need: Whereas DL focuses on modeling what we already know, EC focuses on creating new knowledge." This is just wrong.
antirez 9y ago
Optimizing genes leading to an intelligent neural network is what biology did basically. If your optimization problem changes the behavior of something able to compute in order to maximize the ability to solve a task, then that's inventing things IMHO.
- IanCal 9y ago
  Which is then no different to DL or any other learning algorithm.
- pmalynin 9y ago
  Respectfully, We've had "learning-to-learn" algorithms for a few years now. Including LSTMs that can be learn the gradients for other LSTMs, and deep-RL algorithms that can optimize neural networks. Its hard to say its inventing new things... there is still a clear goal - a loss - and we are optimizing it; poorly in the case of genetic algorithms. I would say genetic methods are simply poorly described reinforcement learning problems, which means that there is a 1-to-1 mapping between Deep Learning and Genetic Algorithms. PS. Making a distinction between "Genetic Algorithms" and "Genetic Programming" is like calling Deep Learning "Differential Programming" -- changing the name of a thing does not change the thing itself.
  - antirez 9y ago
    I don't want to mean that this is "novel" at all, I played a lot with genetic programming like 20 years ago. However in my opinion genetic algorithms and genetic programming are not the same thing, while closely related: genetic programming is using genetic algorithms as a search strategy to find a way to write a program in order to solve a problem. It is more closely related to reinforcement learning in theory, you just have a problem, a fitness function, and you provide no hints about how to solve it. Note how this is fundamentally different than having already an algorithm in lack of good parameters, which is what GA does. When such parameters are computer instructions, things start to be semantically interesting. However there is a big difference between reinforcement learning and genetic programming: the second will output a program that can be simplified and understood. NNs are much more opaque so even when they outperform known techniques, what they do is not clear. A more practical example: 20 years ago I wrote a GP framework based on a simple stack language (so that programs are always valid, it's an alternative to use S-expressions). Then I used it in order to generate a new hashing function to minimize the collision I had in my hash table. The output of the GP was a code snippet that I could understand, translate into a specification, re-write in C. It effectively invented a good hashing function. You can see everything as an optimization problem, but at the end of the day if the output of a GP is similar to the output of a mathematician that you hired to write a better hash function, well, this is kinda questionable if it was just optimizing or if while doing it the program invented something. Also note that in the 90s things produced by genetic programming went patented, because they were novel algorithms.
fnl 9y ago
While I agree that the quotes you made sound more like marketing than science, antirez has a point here: They might be using GA to evolve a new activation function represented as a GP tree. But that's just my guess. And even so, I am not terribly convinced that would lead to astonishingly new activation functions that are better than the known ones.
Eliezer 9y ago
Gradient descent: Run the neural net, evaluate the fitness function, shift parameters in the direction of the derivative of the fitness function. Genetic algorithm: Shift parameters in a batch of random ways, evaluate using fitness function, keep the best. There is nothing more fundamentally 'creative' about one than the other; these are both just attacks on the problem "here is this function, find parameter settings that get higher scores in it".
- eafan 9y ago
  An important difference is the manner by which 'exploration' is codified in evolutionary algorithms, which allows for these algorithms to have a tune-able mix of hill-climbing and niche-finding. RL-based systems, Evolutionary Strategies, Swarm Intelligence, ... all are essentially hill-climbing and are typically much better at the 'exploit' aspect than the 'explore' aspect of the search. When you pair evolutionary algorithms with novelty search, for instance, you get very good coverage, as these guys have done with their sorting network work.
jacques_chester 9y ago
Antirez distinguished between genetic algorithms and genetic programming. As you say, GAs evolve parameters to the fitness function. But GP does, in fact, evolve programs. They are very distinct schools of work that evolved independently. Some other major schools are Learning Classifier Systems, Evolution Strategies and Evolutionary Programming. The high-level name these days is Evolutionary Computation. In turn typically lumped under Nature-Inspired Computing, or sometimes Metaheuristics.

nickpsecurity 9y ago

To support your claim, here's plenty of examples of GP inventing new things: http://www.genetic-programming.org/combined.php http://www.genetic-programming.org/combined.php Here's a fun article from Popular Science on Koza and his "invention machine" that readers might enjoy: https://www.popsci.com/scitech/article/2006-04/john-koza-has-built-invention-machine https://www.popsci.com/scitech/article/2006-04/john-koza-has...