neat.Population.run() does not return the best genome

[jongdetim]

Describe the bug
After training, the fittest genome is often not the one that was returned. Instead it seems to return the strongest genome from the final generation. I noticed that the top fitness during training can be higher than the genome returned by neat.Population.run().

To Reproduce
Steps to reproduce the behavior:

1. Configure any model with a neat.StdOutReporter reporter
2. Train the model, such as with:
   pe = neat.ParallelEvaluator(multiprocessing.cpu_count(), evaluate_genome)
   winner = population.run(pe.evaluate, 20)
3. Observe the reported species MAX fitness genomes during training, and notice that the highest fitness if often not the one that population.run() returned.

Expected behavior
The genome returned by Population.run() should be the fittest genome it has seen at any point during training.

Stdout example

In the following output snippet, we can see that the max fitness of both species 2 and 3 is 22.0, however, the training function returns a genome with fitness: 17.0:

...
****** Running generation 18 ******

Population's average fitness: 2.98333 stdev: 4.80101
Best fitness: 22.00000 - size: (8, 19) - species 2 - id 956
Average adjusted fitness: 0.131
Mean genetic distance 2.401, standard deviation 0.954
Population of 59 members in 3 species:
ID age size fitness adj fit stag
==== === ==== ======= ======= ====
1 18 14 5.0 0.091 5
2 18 23 22.0 0.146 0
3 14 22 22.0 0.156 1
Total extinctions: 0
Generation time: 0.744 sec (0.499 average)

****** Running generation 19 ******

Population's average fitness: 2.79661 stdev: 3.76325
Best fitness: 17.00000 - size: (8, 19) - species 2 - id 956
Average adjusted fitness: 0.163
Mean genetic distance 2.369, standard deviation 1.078
Population of 61 members in 3 species:
ID age size fitness adj fit stag
==== === ==== ======= ======= ====
1 19 17 7.0 0.155 6
2 19 24 17.0 0.189 1
3 15 20 15.0 0.144 2
Total extinctions: 0
Generation time: 0.757 sec (0.531 average)
winner fitness: 17
Key: 956

Desktop (please complete the following information):

• OS: Windows 10 64-bit
• Python 3.9.6 64-bit
• neat-python 0.92

[jongdetim]

I can also confirm the same happens when using a single process, instead of multiprocessing.
Strangely, looking through population.py, everything seems normal, as it returns self.best_genome.

My current workaround is instead calling population.reporters.reporters[0].best_genome() to get the best all-time genome.

[CodeReclaimers]

Thank you for reporting this, especially the detailed reproduction! This code is long overdue for an overhaul after all the changes I've merged into it in the last couple years. I will try to find the time to get these issues addressed in a minor release soon, and leave the deep dive for later this year.