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Ask HN: Is genetic programming still actively researched?

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

#11

Post by aaron695 »

Intuitively to me Monty Carlo would be better. Monty Carlo rocks.

Intuitively to me intelligent design is going to beat genetic programming.

It's the constants and knowing which intelligently designed algorithm is better that is impossible to know, which Monty Carlo solves.

Look at the antenna designed on Wiki and think how easy that would be with Monty Carlo - https://en.wikipedia.org/wiki/Genetic_algorithm#:~:text=The%....

John R. Koza book was 1992, computational power now allows us to smash things.

Here's a comparison at a wind farm design between Monte Carlo and genetic algorithms (Monty Carlo was better) - https://rera.shahroodut.ac.ir/article_2146_5e7bee97938fcd513...

But it's really interesting, have fun looking into it. Have a look through HN articles - https://hn.algolia.com/?dateRange=all&page=0&prefix=true&que...

[edit] I haven't differentiated between "What are the differences between genetic algorithms and genetic programming?" - https://stackoverflow.com/questions/3819977/what-are-the-dif...

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

#12

Post by qubex »

versteegen wrote: post #6

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…

It doesn’t actually surprise me that hybrid genetic/memetic approaches outcompete purely genetic approaches: after all, hybrid genetic/memetic humans have outcompeted purely genetic species at every level.

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

#14

Post by bionhoward »

Yes it is going on. I wound up having to write a new python GP library from scratch due to DEAP license being gpl3. Now I’m translating that to Rust. One point of order: don’t only try stuff randomly. Also try trying stuff in shortlex order. Also, make sure you use hyperparameter optimization on the outside of the GP evolution process or else you’ll wind up with too many parameters to hand-tune.

I think the link between Pascal’s Simplex, Koza GP Tree Words, and Levin Search, is fascinating.

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

#17

Post by kuhewa »

qubex wrote: post #12
versteegen wrote: post #6

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…

It doesn’t actually surprise me that hybrid genetic/memetic approaches outcompete purely genetic approaches: after all, hybrid genetic/memetic humans have outcompeted purely genetic species at every level.

Not every level

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

#18

Post by dadoomer »

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.

This a bit of a false dichotomy: genetic algorithms need not forego of directed or local search (for example, memetic and hybrid algorithms mix gradient search with populations).

Also, you have to keep in mind, it is often very hard to frame problems in a way that makes linear regression or gradient descent practical.

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

#20

Post by MagicMoonlight »

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.

But how do you know you’re in the right area? You could be linearly improving a really terrible solution, whereas random chance could take you somewhere completely new and better.
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