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TheGreatHomer t1_j41i6hm wrote

I'm pretty sure it's not ML by definition. Oxford definition:

the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data.

There is no data(set) involved in evolutionary algorithms, so it's not ML. Genetic algorithms are usually seen as (a part of) AI, though.

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pucklermuskau t1_j41nsa8 wrote

What are you trying to say by saying there's no dataset? There is data, in the orientation of the structure.

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TheGreatHomer t1_j41rida wrote

There is no dataset from which you learn patterns. You usually evaluate objects which are then again used for mutation based on their performance.

Of course it's not happening in a vacuum, but that's not what "data" usually means.

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lavaboosted OP t1_j41w1fi wrote

The data is the curve generated by each leg orientation. Each curve in the batch is then scored based on some criteria. If that isn't machine learning then neither is using a neural network and evolutionary algorithm and I think most people would say that it is.

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TheGreatHomer t1_j42zoqf wrote

It generates data. It doesn't take data and learns patterns from that data.

If you have a very specific opinion and get defensive when someone disagrees, why pose it as a question instead of just stating your opinion?

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lavaboosted OP t1_j430t1u wrote

I'm not trying to be defensive just wanted to have the discussion and see what other people's takes on this was. What do you think of the car example?

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TheGreatHomer t1_j433r8m wrote

>What do you think of the car example

I haven't read the paper, but only watched the brief video. I wouldn't say that's Machine Learning either.

Maybe a bad analogy but one I can come up with on a spot: A hinge isn't carpentry but metalwork and pretty much everyone agrees on that. Now if you build a wooden cabinet, you are probably using hinges; Nevertheless, you'd still call the cabinet as such carpentry, not metalwork.

Anyway, the definitions aren't clear and consistent enough to make super good and objectively true distinctions. In the end it often boils down to personal subjective interpretations.

Edit: Especially the classification of evolutionary algorithms has been an ongoing discussion for, like, decades. Which goes to show that there probably isn't an objectively right clear classification - if only because people don't agree on a single definition of Machine Learning as is. However, by the most common definitions that I know, evolutionary computation is its own subfield next to ML.

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pucklermuskau t1_j44znly wrote

it takes data, and evaluates that data against a performance metric, and then adapts the structure in response, creating new data.

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