Implement position crossover operator

src/genetic_algorithm.py

@@ -1,5 +1,5 @@
 from numpy import sum, append, arange, delete, where
-from numpy.random import randint, choice
+from numpy.random import randint, choice, shuffle
 from pandas import DataFrame
 
 
@@ -44,14 +44,22 @@ def evaluate_element(element, data):
     return sum(fitness)
 
 
-def select_random_genes(matching_genes, parents, m):
+def select_distinct_genes(matching_genes, parents, m):
     cutoff = randint(m)
-    distinct_genes = delete(arange(m), matching_genes)
-    first_parent_genes = parents[0].point.iloc[distinct_genes[cutoff:]]
-    second_parent_genes = parents[1].point.iloc[distinct_genes[:cutoff]]
+    distinct_indexes = delete(arange(m), matching_genes)
+    first_parent_genes = parents[0].point.iloc[distinct_indexes[cutoff:]]
+    second_parent_genes = parents[1].point.iloc[distinct_indexes[:cutoff]]
     return first_parent_genes, second_parent_genes
 
 
+def select_random_genes(matching_genes, parents, m):
+    random_parent = parents[randint(len(parents))]
+    distinct_indexes = delete(arange(m), matching_genes)
+    genes = random_parent.point.iloc[distinct_indexes].values
+    shuffle(genes)
+    return genes
+
+
 def repair_offspring(offspring, parents, m):
     while len(offspring) != m:
         if len(offspring) > m:
@@ -64,6 +72,7 @@ def repair_offspring(offspring, parents, m):
             offspring = offspring.append(
                 {"point": best_point, "distance": 0}, ignore_index=True
             )
+            random_parent.drop(index=best_index, inplace=True)
     return offspring
 
 
@@ -73,20 +82,31 @@ def get_matching_genes(parents):
     return where(first_parent == second_parent)
 
 
-def uniform_crossover(parents, m):
+def populate_offspring(values):
     offspring = DataFrame(columns=["point", "distance"])
-    matching_genes = get_matching_genes(parents)
-    offspring["point"] = parents[0].point.iloc[matching_genes]
-    first_genes, second_genes = select_random_genes(matching_genes, parents, m)
-    offspring["point"] = offspring["point"].append(first_genes)
-    offspring["point"] = offspring["point"].append(second_genes)
+    for element in values:
+        aux = DataFrame(columns=["point", "distance"])
+        aux["point"] = element
+        offspring = offspring.append(aux)
     offspring["distance"] = 0
+    offspring = offspring[1:]
+    return offspring
+
+
+def uniform_crossover(parents, m):
+    matching_indexes = get_matching_genes(parents)
+    matching_genes = parents[0].point.iloc[matching_indexes]
+    first_genes, second_genes = select_distinct_genes(matching_genes, parents, m)
+    offspring = populate_offspring(values=[matching_genes, first_genes, second_genes])
     viable_offspring = repair_offspring(offspring, parents, m)
     return viable_offspring
 
 
-def position_crossover(parents, n):
-    genotypes = [parents[0].point.values, parents[1].point.values]
+def position_crossover(parents, m):
+    matching_genes = get_matching_genes(parents)
+    shuffled_genes = select_random_genes(matching_genes, parents, m)
+    offspring = populate_offspring(values=[matching_genes, shuffled_genes])
+    return offspring
 
 
 def crossover(mode, parents, m):