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Who's to say what humans do is significantly different from what Eureqa does? We constantly attack our models with new data, but effectively are using data to generate and test new models in the same way Eureqa is. It's certainly a simplified process of equation generation, and model comparison is a significantly open field of statistics, but the rudimentary procedure isn't especially strange.

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Eureqa doesn't help in this regard. As the OP said, you will eventually find a giant model that is a brilliant fit for the data. What you need is some notion of regularization. To answer your comment, it doesn't deal with overfitting.

Except that it does. I'm not too familiar with the application itself, however I know the original research worked by finding pareto optimums based on the predictive power and the simplicity of the equation.

So for double pendulum it came back with like 8 equations, one of which was conservation of momentum (not actually accurate, but as close as you can get with that number of terms), and one of which was conservation of energy (actually accurate, but a bit more complicated).

I'm pretty sure that it does have regularization. The optimization process penalizes model complexity in addition to maximizing the fit to the data.

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