Can we use genetic algorithms to evolve datasets? Genetic algorithms are used to solve many optimization tasks.
If I have a dataset, can I evolve it with a genetic algorithm to create an evolved version of the same dataset?
We could consider each feature of the initial dataset as a chromosome (or individual), which is then combined with other chromosomes (features) to find more features. Is this possible? Has this been done?
I will like to edit the details with an example so that it is easier to understand.
Example: In practice cyber-security attacks evolve over time since it finds a new way to breach a system. The main draw-back of intrusion detection model is that it needs to be trained every time attack evolves. So I was hoping if genetic algorithm can be used on the present benchmarked datasets (like NSL-KDD) to come up with a futuristic type dataset maybe after X-number of generations.
And check if a model is able to classify that generated dataset as well.
This question raises a lot more questions. It seems like a solution looking for a problem, instead of the other way round.

How do you measure the fitness of a feature?
What would one of the &quot;evolved datasets&quot; mean? What does it represent?
What would your overal purpose be? If you just wish to generate simulated datasets, there are easier ways to do this, with more control over the various aspects of the resulting datasets.

If you want to compute a new set of features to &quot;better&quot; describe a given dataset, there are many approaches to this, such as PCA, ISOMAP, self-organizing maps, ... If this is the kind of thing you're thinking about, I would recommend starting there.