I would not necessarily call it genetic programming. But you can discover the same pattern e.g. for human knowledge. We have a domain language for math and when we go deeper we will have domain language for algebra, discrete math etc.. and we could go even further to breaking it to even for more specific areas simply to compact the information to deliver. The goal is more and precise information with less words, it might be for communication or to be able to fit the knowledge into the processing capacity.
So for me it is not like the advances in programing languages, but more like to compacting information/representation with domain language. It's similar but not quite genetic programming.
I am not calling it GP. I am pointing out that AI-coding systems tend to resemble GP systems in terms of both broad architecture, and in terms of how the code/genome is generated. Also, I already mentioned domain specific languages, in the context of GP, and how (effectively) creating one (i.e. selecting only relevant genes/verbs) that was a better match for the problem domain, made the GP find a solution infinitely faster[0]. Clearly these two things are not contradictory, and are clearly related, to the point that they can be combined. I am not sure what about my comment gave you the impression that I thought one was contradictory with the other. Hope this follow-up clears things up.
[0]: Because, in effect, we are decreasing the size of the search-space. Every programming-language project is basically a set of bets, that the design choices will decrease the size of the search space for the users of that language (most Unix commands fit this pattern, as do most well designed APIs). Mathematics is similar, in that the notation is specialized and sparse (even the named constants and variables have symbolic names, instead of meaningful names). Most system dynamics models could have the names of entities replaced with id-numbers, and they would continue to function. The whole point of (for example) a library is to give a tiny parameterized notation/language, that the user can use to express a solution to their problem, in terms of the problem domain itself. This decreasing of the search-space, is an act of compression. I have to say, I am a little confused by your reply, because it has the tone of a disagreement, but it does not really disagree with anything that I have written. Also, I have given various examples to support my analogies, but you have offered zero examples, and I suspect that there would be less confusion (on my part) if you did.
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I would not necessarily call it genetic programming. But you can discover the same pattern e.g. for human knowledge. We have a domain language for math and when we go deeper we will have domain language for algebra, discrete math etc.. and we could go even further to breaking it to even for more specific areas simply to compact the information to deliver. The goal is more and precise information with less words, it might be for communication or to be able to fit the knowledge into the processing capacity. So for me it is not like the advances in programing languages, but more like to compacting information/representation with domain language. It's similar but not quite genetic programming.
I am not calling it GP. I am pointing out that AI-coding systems tend to resemble GP systems in terms of both broad architecture, and in terms of how the code/genome is generated. Also, I already mentioned domain specific languages, in the context of GP, and how (effectively) creating one (i.e. selecting only relevant genes/verbs) that was a better match for the problem domain, made the GP find a solution infinitely faster[0]. Clearly these two things are not contradictory, and are clearly related, to the point that they can be combined. I am not sure what about my comment gave you the impression that I thought one was contradictory with the other. Hope this follow-up clears things up.
[0]: Because, in effect, we are decreasing the size of the search-space. Every programming-language project is basically a set of bets, that the design choices will decrease the size of the search space for the users of that language (most Unix commands fit this pattern, as do most well designed APIs). Mathematics is similar, in that the notation is specialized and sparse (even the named constants and variables have symbolic names, instead of meaningful names). Most system dynamics models could have the names of entities replaced with id-numbers, and they would continue to function. The whole point of (for example) a library is to give a tiny parameterized notation/language, that the user can use to express a solution to their problem, in terms of the problem domain itself. This decreasing of the search-space, is an act of compression. I have to say, I am a little confused by your reply, because it has the tone of a disagreement, but it does not really disagree with anything that I have written. Also, I have given various examples to support my analogies, but you have offered zero examples, and I suspect that there would be less confusion (on my part) if you did.