# How can I calculate MBF in genetic algorithms?

I've just started to learn genetics algorithms and I have found these measurements of runs that I don't understand:

MBF: The mean best fitness measure (MBF) is the average of the best fitness values over all runs. AES: The average number of evaluation to solution.

I have an initial random population. To evolve a population I do:

1. Tournament selection
2. One point crossover.
3. Random resetting.
4. Age based replacement with elitism (I replace the population with all offsprings generated).
5. If I have generated G generations (in other words, I have repeated this four points G times) or I have found the solution, the algorithm ends, otherwise, it comes back to point 1.

Is the mean of the best fitness the mean fitness of all of each generations (G best fitness)?

MBF = (BestFitness_0 + ... + BestFitness_G) / G


I'm not English and I don't understand the meaning of run here.