Genetic Algorithm: String to List Individuals (aimacode#523)

antmarakis · norvig · commit 11bde60100bb · 2017-05-30T21:20:28.000-07:00
* Update search.py

* Update test_search.py
diff --git a/search.py b/search.py
@@ -582,7 +582,7 @@ def genetic_search(problem, fitness_fn, ngen=1000, pmut=0.1, n=20):
     return genetic_algorithm(states[:n], problem.value, ngen, pmut)
 
 
-def genetic_algorithm(population, fitness_fn, gene_pool=['0', '1'], f_thres=None, ngen=1000, pmut=0.1):  # noqa
+def genetic_algorithm(population, fitness_fn, gene_pool=[0, 1], f_thres=None, ngen=1000, pmut=0.1):  # noqa
     """[Figure 4.8]"""
     for i in range(ngen):
         new_population = []
@@ -610,13 +610,11 @@ def init_population(pop_number, gene_pool, state_length):
     """Initializes population for genetic algorithm
     pop_number  :  Number of individuals in population
     gene_pool   :  List of possible values for individuals
-                   (char only)
     state_length:  The length of each individual"""
     g = len(gene_pool)
     population = []
     for i in range(pop_number):
-        new_individual = ''.join([gene_pool[random.randrange(0, g)]
-                                  for j in range(state_length)])
+        new_individual = [gene_pool[random.randrange(0, g)] for j in range(state_length)]
         population.append(new_individual)
 
     return population
@@ -635,7 +633,7 @@ def mutate(x, gene_pool):
     r = random.randrange(0, g)
 
     new_gene = gene_pool[r]
-    return x[:c] + new_gene + x[c+1:]
+    return x[:c] + [new_gene] + x[c+1:]
 
 # _____________________________________________________________________________
 # The remainder of this file implements examples for the search algorithms.
diff --git a/tests/test_search.py b/tests/test_search.py
@@ -96,16 +96,21 @@ def test_genetic_algorithm():
         'D': [2, 3]
     }
 
-    population = init_population(8, ['0', '1'], 4)
-
     def fitness(c):
         return sum(c[n1] != c[n2] for (n1, n2) in edges.values())
 
-    solution = genetic_algorithm(population, fitness)
-    assert solution == "0101" or solution == "1010"
+    solution_chars = GA_GraphColoringChars(edges, fitness)
+    assert solution_chars == ['R', 'G', 'R', 'G'] or solution_chars == ['G', 'R', 'G', 'R']
+
+    solution_bools = GA_GraphColoringBools(edges, fitness)
+    assert solution_bools == [True, False, True, False] or solution_bools == [False, True, False, True]
+
+    solution_ints = GA_GraphColoringInts(edges, fitness)
+    assert solution_ints == [0, 1, 0, 1] or solution_ints == [1, 0, 1, 0]
 
     # Queens Problem
-    population = init_population(100, [str(i) for i in range(8)], 8)
+    gene_pool = range(8)
+    population = init_population(100, gene_pool, 8)
 
     def fitness(q):
         non_attacking = 0
@@ -122,10 +127,31 @@ def fitness(q):
         return non_attacking
 
 
-    solution = genetic_algorithm(population, fitness, f_thres=25)
+    solution = genetic_algorithm(population, fitness, gene_pool=gene_pool, f_thres=25)
     assert fitness(solution) >= 25
 
 
+def GA_GraphColoringChars(edges, fitness):
+    gene_pool = ['R', 'G']
+    population = init_population(8, gene_pool, 4)
+
+    return genetic_algorithm(population, fitness, gene_pool=gene_pool)
+
+
+def GA_GraphColoringBools(edges, fitness):
+    gene_pool = [True, False]
+    population = init_population(8, gene_pool, 4)
+
+    return genetic_algorithm(population, fitness, gene_pool=gene_pool)
+
+
+def GA_GraphColoringInts(edges, fitness):
+    population = init_population(8, [0, 1], 4)
+
+    return genetic_algorithm(population, fitness)
+
+
+
 # TODO: for .ipynb:
 """
 >>> compare_graph_searchers()
