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Ask HN: Is genetic programming still actively researched?

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Ask HN: Is genetic programming still actively researched?

#1

Post by mg »

It is fascinating that neural networks have such a run at the moment. I wonder if this will continue "forever". Or if we will see a different paradigm eclipse them in the future.

Is anybody still doing research in the area of genetic programming?

The genetic programming books of John R. Koza were the first I ever read about machine learning. It felt like magic at that time.

I have the feeling that the approach to generate programs for the CPU via evolution still has a lot to offer if it was explored further.

If there is research going on out there, I would love to follow it.

Re: Ask HN: Is genetic programming still actively researched?

#2

Post by cookiengineer »

The genetic programming scene kind of evolved into NEAT, HyperNEAT and ES-HyperNEAT as a meta learning concept.

Connections between layers/nodes are serialized as genes of agents with phenotypes and dominant/recessive markers, and an observing CPPN learns to categorize agents into different traits to find more efficient breeding mechanisms.

It's a strong concept, and AFAIK it's still used a lot in the robotics world where you have to guarantee behaviors and have to be able to reproduce behaviors due to safety regulations.

There was a nice intro video into the underlying base concept which is called NEAT by a youtuber named SethBling [2]

[1] http://eplex.cs.ucf.edu/ESHyperNEAT/

[2] https://m.youtube.com/watch?v=qv6UVOQ0F44

Re: Ask HN: Is genetic programming still actively researched?

#3

Post by mg »

cookiengineer wrote: post #2

The genetic programming scene kind of evolved into NEAT, HyperNEAT and ES-HyperNEAT as a meta learning concept. Connections between layers/nodes are serialized as genes of agents with phenotypes and dominant/recessive markers, and an observing CPPN learns to categorize agents into different traits to find more efficient breeding mechanisms. It's a strong concept, and AFAIK it's still used a lot in the robotics world…

Thanks for the infos. I will look into it.

Re: Ask HN: Is genetic programming still actively researched?

#5

Post by androidbishop »

One of the reasons I got my degree in Biotechnology was because I realized that the technology of life is mind-bogglingly advanced and learning how it does things can have profound insights into how we solve other problems. The process of mutation and evolution is definitely a strong contender for this, maybe one of the most important and powerful.

Re: Ask HN: Is genetic programming still actively researched?

#6

Post by versteegen »

It's completely mistaken to think that the NN craze means noone works on anything else. Academia has very many people researching whatever they want, full-time or on the side. AI has especially many veteran researchers stubbornly following long-standing lines of research which have unimpressive results. Noone can say they're wrong. Hinton was once that guy doing unfashionable research into NNs.

Anyway, there are also memetic algorithms, which extend genetic algorithms by adding local search (some form of local improvement such as gradient following or simple handcoded heuristics) to the genetic global search. Actually a very simple idea (e.g. alternate mutation and/or recombination and optimisation steps). They tend to perform better than pure genetic algorithms because they can actually use gradient information or heuristics. It's a very broad class of algorithms which tend to have many hyperparameters.

Re: Ask HN: Is genetic programming still actively researched?

#7

Post by jacquesm »

Aicy wrote: post #4

I'm a non-expert here but it seems intuitive to me that directly moving towards an improved model via linear regression is more efficient than randomly changing your model and then running a natural selection simulation to improve fitness.

> directly moving towards an improved model via linear regression is more efficient than randomly changing your model and then running a natural selection simulation to improve fitness.

That is objectively true, but don't underestimate how much of that process is simulated by the way we train our models. The natural selection bit never was natural to begin with (it's obviously artificial), and is the rough equivalent of the final step in training a model: verification on unseen data. If the model performs worse compared to a previous one then it is discarded!

Evolutionary algorithms are somewhat interesting because they can come up with weird stuff that works anyway, that random element can result in entirely novel approaches (to the point that we have a hard time to understand what is going on) and that's something that I have not seen with neural nets.

There are some interesting hybrids:

https://www.sciencedirect.com/science/article/abs/pii/S09521...

Re: Ask HN: Is genetic programming still actively researched?

#8

Post by jacquesm »

androidbishop wrote: post #5

One of the reasons I got my degree in Biotechnology was because I realized that the technology of life is mind-bogglingly advanced and learning how it does things can have profound insights into how we solve other problems. The process of mutation and evolution is definitely a strong contender for this, maybe one of the most important and powerful.

That's a very wise decision. We are far from done with learning from nature. As more and more of the DNA is unlocked it never ceases to amaze me how incredibly complex it all is and how many parts of the process interact with each others, sometimes across several levels of abstraction.

Re: Ask HN: Is genetic programming still actively researched?

#10

Post by deadly_penguin »

Yes, although it is much more active in robotics. York still has quite active research into evolutionary algorithms and genetic programming (https://www.york.ac.uk/physics-engineering-technology/resear...).

It's been used to do things like find design parameters (https://pure.york.ac.uk/portal/en/publications/evolving-desi...) and attempt to evolve robots to fit an environment (https://www.york.ac.uk/robot-lab/are/)

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