How to summarize knowledge about importance of variables? Assume that we have 30 features (inputs) each of which can potentially influence the result (output). We try to use the available ("observed") mapping from features to targets to develop a predictive model that uses only some of the features. Each feature can be either included into the input of the model or not. It means that in total we have 2^30 combinations of what arguments to use. I would like to notice that model is fixed, I do not change the model (in a broad sense), I just decide what variables to use.
Now we have considered several thousand combinations and for each of the combinations we have a measure of how good it is (for that, of course, we use the testing set). For example, we know that if we take variable 1, 7, 13, and 27 we get an error (accuracy of the model) that is equal to 123.456. If we use variable 3, 4, 10, 11, 13, 27 and 29. We get an error equal to 23.456.
My question is: How to summarize the knowledge about performance of the model as a function of the used arguments?
In other words I need a "meta-model" that describes performance of my models. I need this because I want to use the obtained knowledge to search over the huge space of potential models more intelligently.
More specifically, I have a mapping from N binary vectors to a real (float) value. Each binary vector indicates if a corresponding variable is used or not and the real output describes the performance of the corresponding model.  Now I want to use this "meta-data" to train a "meta-model". But before I start a "meta-training" I would like to decide what model is a good one to train. This model should capture such concepts as "information stored in variables". For example I can imagine that variable 7 has no additional information to the combination of variables 3 and 5. Or, we can have "orthogonal" variables that cover the target space "independently".
 A: In case you are interested in a linear model, here's direction that hasn't been mentioned in the comments / other answers:
Let $x \in R^{30}$ be the feature vector, and you want to learn a linear mapping $y = w\cdot x$ that maps $x$ to a value (that can be transformed into a probability using logistic regression, from which you can do anything). 
I assume that there's a cost involved in obtaining many features / using all of them for prediction (otherwise, why not use all of them).
What you can do is to regularize the learning process with $L_1$ norm. It is known that $L_1$ regularization results in sparser models (i.e. a weight vector $w$ with lots of zeros in it), which is what you want.
Specifically, you would minimize the following:
$$ \textrm{RegularizedLoss}(w,\lambda) = \textrm{Loss}(w) + \lambda \cdot \|w\|_1,$$
where $\textrm{Loss}(w)$ is the logistic loss. There are efficient ways to do this (for example, vowpal wabbit can handle $L_1$ regularization).
The specific value of $\lambda$ controls the tradeoff between accuracy and sparsity, i.e. how much loss in accuracy are you willing to take in return for a model that uses less features.
HTH.
A: Whenever I approach problem of optimization function that I cannot define, but I can calculate, I use some heuristic method.
As your searched space is big (but not BIG big, 30 features is not a lot), I would go with some genetic algorithm. This could be quite trivial, but representation of your problem may just be binary vector, so standard masked crossover and bit flipping mutation may have quite good results.
As for your question specifically: you can summarize this knowledge in form of GA itself. You do not need human-readable representation of your knowledge, so actual knowledge will be encoded in specimen representation and evaluation function.
Disclaimer: This is my intuition, close to guessing (but with some experience in heuristic optimization). I cannot show you any papers that would back this idea, but I'm pretty sure you'll find something on that.
