Typical Number of generations for NEAT (Neuro Evolution of Augmenting Topologies)?
There are a couple other hyperparameters to consider before deciding NEAT cannot produce a usable neural network for your problem. You will have to make sure that your population is also large enough. Obviously a larger dataset is more helpful, but that is limited. Finally, changes such as mutation rates, aggregation options, activation functions, and your fitness function will all affect the training process for each genome. Feel free to PM if you want suggestions on them.
Does anyone have an estimate of the number of generations one should search before concluding that the NEAT-algorithm is not able to reach the minima?
I am running NEAT on a very small dataset of cancer patients (~5K rows). And after 5000 generations, the concordance index for prediction of survival index is not improving. 
Does anyone have any experience of how many generations should one try before you deem this as not efficient for the given problem?