The late Thomas Cover , (likely the leading "Information Theorist" of his generation), considered "Universal" approaches to things like data compression and portfolio allocations as true genetic algorithms.

Evolution has no parameters to fit or train. Why should true genetic algorithms?

Universal approaches make no assumptions about the underlying distribution of data. They make no attempt to predict the future from patterns or anything else.

The "theoretical" effectiveness of Universal approaches (they present significant implementation challenges see my recent question: Geometry for Universal Portfolios?) follow from them doing what evolution demands. The fastest, smartest, or strongest don't necessarily survive in the next generation. Evolution favors that gene, organism, meme, portfolio, or data compression algorithm positioned to most easily adapt to whatever happens next.

Also, because these approaches make make no assumptions and operate non-parametrically, one can consider all tests, even on all historical data, as out-of-sample.

Certainly they have limitations, Certainly they can't work for every kind a problem we face in our domain, but gee, what an interesting way to think about the things.

The late Thomas Cover , (likely the leading "Information Theorist" of his generation), considered "Universal" approaches to things like data compression and portfolio allocations as true genetic algorithms.

Evolution has no parameters to fit or train. Why should true genetic algorithms?

Universal approaches make no assumptions about the underlying distribution of data. They make no attempt to predict the future from patterns or anything else.

The "theoretical" effectiveness of Universal approaches (they present significant implementation challenges see my recent question: Geometry for Universal Portfolios?) follow from them doing what evolution demands. The fastest, smartest, or strongest don't necessarily survive in the next generation. Evolution favors that gene, organism, meme, portfolio, or data compression algorithm positioned to most easily adapt to whatever happens next.

Also, because these approaches make make no assumptions and operate non-parametrically, one can consider all tests, even on all historical data, as out-of-sample.

Certainly they have limitations, Certainly they can't work for every kind a problem we face in our domain, but gee, what an interesting way to think about the things.