Implement mutation operator

src/genetic_algorithm.py

@@ -1,6 +1,7 @@
 from numpy import sum, append, arange, delete, where
 from numpy.random import randint, choice, shuffle, random
 from pandas import DataFrame
+from math import ceil
 
 
 def get_row_distance(source, destination, data):
@@ -16,9 +17,7 @@ def compute_distance(element, solution, data):
     distinct_elements = solution.query(f"point != {element}")
     for _, item in distinct_elements.iterrows():
         accumulator += get_row_distance(
-            source=element,
-            destination=item.point,
-            data=data,
+            source=element, destination=item.point, data=data
         )
     return accumulator
 
@@ -120,16 +119,25 @@ def element_in_dataframe(solution, element):
     return not duplicates.empty
 
 
-def mutate(solution, n, probability=0.001):
-    if random() > probability:
-        return solution
-    row = solution.sample()
-    random_element = randint(n)
-    while element_in_dataframe(solution=solution, element=random_element):
-        random_element = randint(n)
-    solution["point"].iloc[row.index] = random_element
-    solution["distance"].loc[row.index] = 0
-    return solution
+def select_new_gene(individual, n):
+    while True:
+        new_gene = randint(n)
+        if not element_in_dataframe(solution=individual, element=new_gene):
+            return new_gene
+
+
+def mutate(population, n, probability=0.001):
+    expected_mutations = len(population) * n * probability
+    individuals = []
+    genes = []
+    for _ in range(ceil(expected_mutations)):
+        individuals.append(randint(n))
+        genes.append(population[individuals[-1]].sample().index)
+    for ind, gen in zip(individuals, genes):
+        individual = population[ind]
+        individual["point"].iloc[gen] = select_new_gene(individual, n)
+        individual["distance"].iloc[gen] = 0
+    return population
 
 
 def tournament_selection(solution):