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GitHub actions #286

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merged 6 commits into from
Apr 14, 2024
Merged

GitHub actions #286

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51c1bca
Change parameter name
ahmedfgad Feb 19, 2024
0734736
Merge branch 'github-actions' of https://github.com/ahmedfgad/Genetic…
ahmedfgad Feb 19, 2024
2daae49
Fix warning
ahmedfgad Mar 27, 2024
a846a26
Fix adaptive mutation desc
ahmedfgad Mar 28, 2024
4eba80b
Reduce time of single point crossover
ahmedfgad Mar 28, 2024
8bbc4c8
Reduce time of crossover functions
ahmedfgad Mar 28, 2024
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6 changes: 4 additions & 2 deletions docs/source/pygad.rst
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Expand Up @@ -927,8 +927,10 @@ shuffles their order randomly.
``adaptive_mutation()``
~~~~~~~~~~~~~~~~~~~~~~~

Applies the adaptive mutation which selects a subset of genes and
shuffles their order randomly.
Applies the adaptive mutation which selects the number/percentage of
genes to mutate based on the solution's fitness. If the fitness is high
(i.e. solution quality is high), then small number/percentage of genes
is mutated compared to a solution with a low fitness.

.. _bestsolution:

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2 changes: 1 addition & 1 deletion examples/example_multi_objective.py
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Expand Up @@ -53,7 +53,7 @@ def on_generation(ga_instance):
# Running the GA to optimize the parameters of the function.
ga_instance.run()

ga_instance.plot_fitness(labels=['Obj 1', 'Obj 2'])
ga_instance.plot_fitness(label=['Obj 1', 'Obj 2'])

# Returning the details of the best solution.
solution, solution_fitness, solution_idx = ga_instance.best_solution(ga_instance.last_generation_fitness)
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5 changes: 3 additions & 2 deletions pygad/pygad.py
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Expand Up @@ -1135,8 +1135,9 @@ def __init__(self,

# Validate delay_after_gen
if type(delay_after_gen) in GA.supported_int_float_types:
if not self.suppress_warnings:
warnings.warn("The 'delay_after_gen' parameter is deprecated starting from PyGAD 3.3.0. To delay or pause the evolution after each generation, assign a callback function/method to the 'on_generation' parameter to adds some time delay.")
if delay_after_gen != 0.0:
if not self.suppress_warnings:
warnings.warn("The 'delay_after_gen' parameter is deprecated starting from PyGAD 3.3.0. To delay or pause the evolution after each generation, assign a callback function/method to the 'on_generation' parameter to adds some time delay.")
if delay_after_gen >= 0.0:
self.delay_after_gen = delay_after_gen
else:
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2 changes: 1 addition & 1 deletion pygad/utils/__init__.py
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Expand Up @@ -3,4 +3,4 @@
from pygad.utils import mutation
from pygad.utils import nsga2

__version__ = "1.1.1"
__version__ = "1.2.1"
83 changes: 54 additions & 29 deletions pygad/utils/crossover.py
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Expand Up @@ -25,23 +25,27 @@ def single_point_crossover(self, parents, offspring_size):
else:
offspring = numpy.empty(offspring_size, dtype=object)

for k in range(offspring_size[0]):
# The point at which crossover takes place between two parents. Usually, it is at the center.
crossover_point = numpy.random.randint(low=0, high=parents.shape[1], size=1)[0]
# 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 All @@ -51,9 +55,9 @@ def single_point_crossover(self, parents, offspring_size):
parent2_idx = (k+1) % parents.shape[0]

# The new offspring has its first half of its genes from the first parent.
offspring[k, 0:crossover_point] = parents[parent1_idx, 0:crossover_point]
offspring[k, 0:crossover_points[k]] = parents[parent1_idx, 0:crossover_points[k]]
# The new offspring has its second half of its genes from the second parent.
offspring[k, crossover_point:] = parents[parent2_idx, crossover_point:]
offspring[k, crossover_points[k]:] = parents[parent2_idx, crossover_points[k]:]

if self.allow_duplicate_genes == False:
if self.gene_space is None:
Expand Down Expand Up @@ -86,17 +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 @@ -106,7 +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 @@ -116,11 +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 @@ -151,10 +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 @@ -164,7 +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 @@ -173,12 +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 @@ -212,10 +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 @@ -225,7 +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 @@ -234,9 +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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