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Reduce time of crossover functions
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@ahmedfgad
ahmedfgad committed Mar 28, 2024
1 parent 4eba80b commit 8bbc4c8
Showing 1 changed file with 47 additions and 24 deletions.
71 changes: 47 additions & 24 deletions pygad/utils/crossover.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,24 +26,26 @@ def single_point_crossover(self, parents, offspring_size):
offspring = numpy.empty(offspring_size, dtype=object)

# Randomly generate all the K points at which crossover takes place between each two parents. The point does not have to be always at the center of the solutions.
# This saves time by calling the numpy.random.randint() function only once.
crossover_points = numpy.random.randint(low=0,
high=parents.shape[1],
size=offspring_size[0])

for k in range(offspring_size[0]):
# Check if the crossover_probability parameter is used.
if not (self.crossover_probability is None):
probs = numpy.random.random(size=parents.shape[0])
indices = numpy.where(probs <= self.crossover_probability)[0]
indices = list(set(numpy.where(probs <= self.crossover_probability)[0]))

# If no parent satisfied the probability, no crossover is applied and a parent is selected.
# If no parent satisfied the probability, no crossover is applied and a parent is selected as is.
if len(indices) == 0:
offspring[k, :] = parents[k % parents.shape[0], :]
continue
elif len(indices) == 1:
parent1_idx = indices[0]
parent2_idx = parent1_idx
else:
indices = random.sample(list(set(indices)), 2)
indices = random.sample(indices, 2)
parent1_idx = indices[0]
parent2_idx = indices[1]
else:
Expand Down Expand Up @@ -88,17 +90,23 @@ def two_points_crossover(self, parents, offspring_size):
else:
offspring = numpy.empty(offspring_size, dtype=object)

# Randomly generate all the first K points at which crossover takes place between each two parents.
# This saves time by calling the numpy.random.randint() function only once.
if (parents.shape[1] == 1): # If the chromosome has only a single gene. In this case, this gene is copied from the second parent.
crossover_points_1 = numpy.zeros(offspring_size[0])
else:
crossover_points_1 = numpy.random.randint(low=0,
high=numpy.ceil(parents.shape[1]/2 + 1),
size=offspring_size[0])

# The second point must always be greater than the first point.
crossover_points_2 = crossover_points_1 + int(parents.shape[1]/2)

for k in range(offspring_size[0]):
if (parents.shape[1] == 1): # If the chromosome has only a single gene. In this case, this gene is copied from the second parent.
crossover_point1 = 0
else:
crossover_point1 = numpy.random.randint(low=0, high=numpy.ceil(parents.shape[1]/2 + 1), size=1)[0]

crossover_point2 = crossover_point1 + int(parents.shape[1]/2) # The second point must always be greater than the first point.

if not (self.crossover_probability is None):
probs = numpy.random.random(size=parents.shape[0])
indices = numpy.where(probs <= self.crossover_probability)[0]
indices = list(set(numpy.where(probs <= self.crossover_probability)[0]))

# If no parent satisfied the probability, no crossover is applied and a parent is selected.
if len(indices) == 0:
Expand All @@ -108,7 +116,7 @@ def two_points_crossover(self, parents, offspring_size):
parent1_idx = indices[0]
parent2_idx = parent1_idx
else:
indices = random.sample(list(set(indices)), 2)
indices = random.sample(indices, 2)
parent1_idx = indices[0]
parent2_idx = indices[1]
else:
Expand All @@ -118,11 +126,11 @@ def two_points_crossover(self, parents, offspring_size):
parent2_idx = (k+1) % parents.shape[0]

# The genes from the beginning of the chromosome up to the first point are copied from the first parent.
offspring[k, 0:crossover_point1] = parents[parent1_idx, 0:crossover_point1]
offspring[k, 0:crossover_points_1[k]] = parents[parent1_idx, 0:crossover_points_1[k]]
# The genes from the second point up to the end of the chromosome are copied from the first parent.
offspring[k, crossover_point2:] = parents[parent1_idx, crossover_point2:]
offspring[k, crossover_points_2[k]:] = parents[parent1_idx, crossover_points_2[k]:]
# The genes between the 2 points are copied from the second parent.
offspring[k, crossover_point1:crossover_point2] = parents[parent2_idx, crossover_point1:crossover_point2]
offspring[k, crossover_points_1[k]:crossover_points_2[k]] = parents[parent2_idx, crossover_points_1[k]:crossover_points_2[k]]

if self.allow_duplicate_genes == False:
if self.gene_space is None:
Expand Down Expand Up @@ -153,10 +161,18 @@ def uniform_crossover(self, parents, offspring_size):
else:
offspring = numpy.empty(offspring_size, dtype=object)

# Randomly generate all the genes sources at which crossover takes place between each two parents.
# This saves time by calling the numpy.random.randint() function only once.
# There is a list of 0 and 1 for each offspring.
# [0, 1, 0, 0, 1, 1]: If the value is 0, then take the gene from the first parent. If 1, take it from the second parent.
genes_sources = numpy.random.randint(low=0,
high=2,
size=offspring_size)

for k in range(offspring_size[0]):
if not (self.crossover_probability is None):
probs = numpy.random.random(size=parents.shape[0])
indices = numpy.where(probs <= self.crossover_probability)[0]
indices = list(set(numpy.where(probs <= self.crossover_probability)[0]))

# If no parent satisfied the probability, no crossover is applied and a parent is selected.
if len(indices) == 0:
Expand All @@ -166,7 +182,7 @@ def uniform_crossover(self, parents, offspring_size):
parent1_idx = indices[0]
parent2_idx = parent1_idx
else:
indices = random.sample(list(set(indices)), 2)
indices = random.sample(indices, 2)
parent1_idx = indices[0]
parent2_idx = indices[1]
else:
Expand All @@ -175,12 +191,11 @@ def uniform_crossover(self, parents, offspring_size):
# Index of the second parent to mate.
parent2_idx = (k+1) % parents.shape[0]

genes_source = numpy.random.randint(low=0, high=2, size=offspring_size[1])
for gene_idx in range(offspring_size[1]):
if (genes_source[gene_idx] == 0):
if (genes_sources[k, gene_idx] == 0):
# The gene will be copied from the first parent if the current gene index is 0.
offspring[k, gene_idx] = parents[parent1_idx, gene_idx]
elif (genes_source[gene_idx] == 1):
elif (genes_sources[k, gene_idx] == 1):
# The gene will be copied from the second parent if the current gene index is 1.
offspring[k, gene_idx] = parents[parent2_idx, gene_idx]

Expand Down Expand Up @@ -214,10 +229,18 @@ def scattered_crossover(self, parents, offspring_size):
else:
offspring = numpy.empty(offspring_size, dtype=object)

# Randomly generate all the genes sources at which crossover takes place between each two parents.
# This saves time by calling the numpy.random.randint() function only once.
# There is a list of 0 and 1 for each offspring.
# [0, 1, 0, 0, 1, 1]: If the value is 0, then take the gene from the first parent. If 1, take it from the second parent.
genes_sources = numpy.random.randint(low=0,
high=2,
size=offspring_size)

for k in range(offspring_size[0]):
if not (self.crossover_probability is None):
probs = numpy.random.random(size=parents.shape[0])
indices = numpy.where(probs <= self.crossover_probability)[0]
indices = list(set(numpy.where(probs <= self.crossover_probability)[0]))

# If no parent satisfied the probability, no crossover is applied and a parent is selected.
if len(indices) == 0:
Expand All @@ -227,7 +250,7 @@ def scattered_crossover(self, parents, offspring_size):
parent1_idx = indices[0]
parent2_idx = parent1_idx
else:
indices = random.sample(list(set(indices)), 2)
indices = random.sample(indices, 2)
parent1_idx = indices[0]
parent2_idx = indices[1]
else:
Expand All @@ -236,9 +259,9 @@ def scattered_crossover(self, parents, offspring_size):
# Index of the second parent to mate.
parent2_idx = (k+1) % parents.shape[0]

# A 0/1 vector where 0 means the gene is taken from the first parent and 1 means the gene is taken from the second parent.
gene_sources = numpy.random.randint(0, 2, size=self.num_genes)
offspring[k, :] = numpy.where(gene_sources == 0, parents[parent1_idx, :], parents[parent2_idx, :])
offspring[k, :] = numpy.where(genes_sources[k] == 0,
parents[parent1_idx, :],
parents[parent2_idx, :])

if self.allow_duplicate_genes == False:
if self.gene_space is None:
Expand Down

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