Ask HN: Can genetic algorithm get “sick”?

By sick, you mean can it become bias to a subspace in the solution space, if so you would "cure" it by re-defining your fitness function to not get "stuck", or fooled by a local maxima.

I don't understand what you mean by "sick". There are a few different ways a genetic algorithm can "fail to converge" though (or converges on a solution that is not a global maxima).

This could be caused by having too high of a diversity, causing the algorithm to give up on promising avenues of evolution too early.

Another risk is having too high of selective pressure, causing the algorithm to get stuck on a "good" solution, but not a global maxima (best solution). A high selective pressure will reduce the total breadth of the search and create a deeper, more focused search on a smaller area (in general).

There are other factors to consider, such as bloating (esp. in genetic programming, where syntax trees can become unmanageably large) and the need for an accurate "fitness" function, less you select for something other than the actual solution.

You may want to look into poisoning attacks or the no-free-lunch theorems for interesting technical aspects of reinforcement learners