Store a deep copy of each generation's best genome, as genomes appear…

… to be modified later, giving incorrect progress results.

NOTE: This is a temporary workaround to make displayed results more accurate, as the modification of a genome after its generation has passed is a bug.

neat/population.py

@@ -1,5 +1,6 @@
 from __future__ import print_function
 
+import copy
 import gzip
 import random
 import time
@@ -197,7 +198,7 @@ def epoch(self, fitness_function, n, report=True, save_best=False, checkpoint_in
             self.__speciate(report)
 
             # Current generation's best genome
-            self.most_fit_genomes.append(max(self.population))
+            self.most_fit_genomes.append(copy.deepcopy(max(self.population)))
             # Current population's average fitness
             self.avg_fitness_scores.append(mean([c.fitness for c in self.population]))