Take the tour ×  I have two implementations of a genetic
algorithm which are supposed to behave equivalently. However due
to technical restrictions which cannot be resolved their output
is not exactly the same, given the same input.  Still I'd like
to show that there is no significant performance difference.  I
have 20 runs with the same configuration for each of the two
algorithms, using different initial random number seeds. For
each run and generation the minimum error fitness of the best
individual in the population was recorded. The algorithm employs
an elite-preserving mechanism, so the fitness of the best
individual is monotonically decreasing. A run consists of 1000
generations, so I have 1000 values per run. I cannot get more
data, as the calculations are very expensive.  Which test should
I employ? An easy way would probably be to only compare the
error in the final generations (again, which test would I use
here)? But one might also think about comparing the convergence
behaviour in general.  share