diff --git a/search.py b/search.py
index d104d7793..4177cd07e 100644
--- 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
index ebc02b5ab..d07edb31e 100644
--- 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()
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