Refactor the max_time_limit on 'target'

algorithms/genetic_algorithm.py

@@ -47,7 +47,7 @@ class Chromosome:
 
 
 class GeneticAlgorithm(BaseAlgorithm):
-    """ Finds nulls by running a genetic algorithm on all possible 
+    """ Finds nulls by running a genetic algorithm on all possible
     discrete values.
     """
 
@@ -69,6 +69,7 @@ class GeneticAlgorithm(BaseAlgorithm):
         self.gen_to_repeat = options.gen_to_repeat
         self.time_limit = options.time_limit
         self.stop_after_score = options.stop_after_score
+        self.max_time_limit = 20 * 1000
 
         self.generations = 0
         self.chromosomes = []
@@ -95,7 +96,11 @@ class GeneticAlgorithm(BaseAlgorithm):
         self.organize_sample()
         solve_function = getattr(self, "solve_" + self.stop_criterion)
         solve_function()
-        return (self.make_weights(self.chromosomes[0]), self.chromosomes[0].get_score(), self.generations)
+        return (
+            self.make_weights(self.chromosomes[0]),
+            self.chromosomes[0].get_score(),
+            self.generations
+        )
 
     def solve_time(self):
         start_time = time_ns()
@@ -104,7 +109,7 @@ class GeneticAlgorithm(BaseAlgorithm):
 
     def solve_target(self):
         start_time = time_ns()
-        while (time_ns() - start_time) // 10**6 <= 10 * 1000:
+        while (time_ns() - start_time) // 10**6 <= self.max_time_limit:
             self.step()
 
     def solve_iter(self):
@@ -115,6 +120,7 @@ class GeneticAlgorithm(BaseAlgorithm):
         self.create_children()
         self.mutate_sample()
         self.organize_sample()
+        self.generations += 1
 
     def create_children(self):
         """Using the better half of the population, creates children overwriting the bottom half by doing crossovers.
@@ -137,8 +143,6 @@ class GeneticAlgorithm(BaseAlgorithm):
                 ) if len(self.buckets[bucket_opp]) > 0 else choice(self.chromosomes)
                 self.crossover_bucket(p1, p2, child, child + 1)
 
-        self.generations += 1
-
     def organize_sample(self):
         """Reorganizes the sample by updating pattern for all chromosomes and sorting them by their scores.
         Optionally, if use_buckets is True, allocates each chromosome to its respective bucket."""