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Merged
merged 5 commits into from
Nov 6, 2023
Merged

GitHub actions #244

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2865a2c
More tests for NSGA-II
ahmedfgad Nov 6, 2023
f7b8bd3
Adaptive mutation bug fix & more tests
ahmedfgad Nov 6, 2023
20fc0f8
More tests
ahmedfgad Nov 6, 2023
d17fe6a
Bug fix
ahmedfgad Nov 6, 2023
c4769bb
Bug fix
ahmedfgad Nov 6, 2023
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2 changes: 1 addition & 1 deletion pygad/pygad.py
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Original file line number Diff line number Diff line change
Expand Up @@ -880,7 +880,7 @@ def __init__(self,
self.select_parents = self.rank_selection
else:
self.valid_parameters = False
raise TypeError(f"Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (for steady state selection)\n-rws (for roulette wheel selection)\n-sus (for stochastic universal selection)\n-rank (for rank selection)\n-random (for random selection)\n-tournament (for tournament selection).\n")
raise TypeError(f"Undefined parent selection type: {parent_selection_type}. \nThe assigned value to the 'parent_selection_type' parameter does not refer to one of the supported parent selection techniques which are: \n-sss (steady state selection)\n-rws (roulette wheel selection)\n-sus (stochastic universal selection)\n-rank (rank selection)\n-random (random selection)\n-tournament (tournament selection)\n-tournament_nsga2: (Tournament selection for NSGA-II)\n-nsga2: (NSGA-II parent selection).\n")

# For tournament selection, validate the K value.
if (parent_selection_type == "tournament"):
Expand Down
80 changes: 71 additions & 9 deletions pygad/utils/mutation.py
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Expand Up @@ -578,10 +578,31 @@ def adaptive_mutation_by_space(self, offspring):
# Adaptive mutation changes one or more genes in each offspring randomly.
# The number of genes to mutate depends on the solution's fitness value.
for offspring_idx in range(offspring.shape[0]):
if offspring_fitness[offspring_idx] < average_fitness:
adaptive_mutation_num_genes = self.mutation_num_genes[0]
## TODO Make edits to work with multi-objective optimization.
# Compare the fitness of each offspring to the average fitness of each objective function.
fitness_comparison = offspring_fitness[offspring_idx] < average_fitness

# Check if the problem is single or multi-objective optimization.
if type(fitness_comparison) in [bool, numpy.bool_]:
# Single-objective optimization problem.
if offspring_fitness[offspring_idx] < average_fitness:
adaptive_mutation_num_genes = self.mutation_num_genes[0]
else:
adaptive_mutation_num_genes = self.mutation_num_genes[1]
else:
adaptive_mutation_num_genes = self.mutation_num_genes[1]
# Multi-objective optimization problem.

# Get the sum of the pool array (result of comparison).
# True is considered 1 and False is 0.
fitness_comparison_sum = sum(fitness_comparison)
# Check if more than or equal to 50% of the objectives have fitness greater than the average.
# If True, then use the first percentage.
# If False, use the second percentage.
if fitness_comparison_sum >= len(fitness_comparison)/2:
adaptive_mutation_num_genes = self.mutation_num_genes[0]
else:
adaptive_mutation_num_genes = self.mutation_num_genes[1]

mutation_indices = numpy.array(random.sample(range(0, self.num_genes), adaptive_mutation_num_genes))
for gene_idx in mutation_indices:

Expand Down Expand Up @@ -703,6 +724,7 @@ def adaptive_mutation_randomly(self, offspring):
## TODO Make edits to work with multi-objective optimization.
# Compare the fitness of each offspring to the average fitness of each objective function.
fitness_comparison = offspring_fitness[offspring_idx] < average_fitness

# Check if the problem is single or multi-objective optimization.
if type(fitness_comparison) in [bool, numpy.bool_]:
# Single-objective optimization problem.
Expand Down Expand Up @@ -791,10 +813,30 @@ def adaptive_mutation_probs_by_space(self, offspring):
# Adaptive random mutation changes one or more genes in each offspring randomly.
# The probability of mutating a gene depends on the solution's fitness value.
for offspring_idx in range(offspring.shape[0]):
if offspring_fitness[offspring_idx] < average_fitness:
adaptive_mutation_probability = self.mutation_probability[0]
## TODO Make edits to work with multi-objective optimization.
# Compare the fitness of each offspring to the average fitness of each objective function.
fitness_comparison = offspring_fitness[offspring_idx] < average_fitness

# Check if the problem is single or multi-objective optimization.
if type(fitness_comparison) in [bool, numpy.bool_]:
# Single-objective optimization problem.
if offspring_fitness[offspring_idx] < average_fitness:
adaptive_mutation_probability = self.mutation_probability[0]
else:
adaptive_mutation_probability = self.mutation_probability[1]
else:
adaptive_mutation_probability = self.mutation_probability[1]
# Multi-objective optimization problem.

# Get the sum of the pool array (result of comparison).
# True is considered 1 and False is 0.
fitness_comparison_sum = sum(fitness_comparison)
# Check if more than or equal to 50% of the objectives have fitness greater than the average.
# If True, then use the first percentage.
# If False, use the second percentage.
if fitness_comparison_sum >= len(fitness_comparison)/2:
adaptive_mutation_probability = self.mutation_probability[0]
else:
adaptive_mutation_probability = self.mutation_probability[1]

probs = numpy.random.random(size=offspring.shape[1])
for gene_idx in range(offspring.shape[1]):
Expand Down Expand Up @@ -914,10 +956,30 @@ def adaptive_mutation_probs_randomly(self, offspring):
# Adaptive random mutation changes one or more genes in each offspring randomly.
# The probability of mutating a gene depends on the solution's fitness value.
for offspring_idx in range(offspring.shape[0]):
if offspring_fitness[offspring_idx] < average_fitness:
adaptive_mutation_probability = self.mutation_probability[0]
## TODO Make edits to work with multi-objective optimization.
# Compare the fitness of each offspring to the average fitness of each objective function.
fitness_comparison = offspring_fitness[offspring_idx] < average_fitness

# Check if the problem is single or multi-objective optimization.
if type(fitness_comparison) in [bool, numpy.bool_]:
# Single-objective optimization problem.
if offspring_fitness[offspring_idx] < average_fitness:
adaptive_mutation_probability = self.mutation_probability[0]
else:
adaptive_mutation_probability = self.mutation_probability[1]
else:
adaptive_mutation_probability = self.mutation_probability[1]
# Multi-objective optimization problem.

# Get the sum of the pool array (result of comparison).
# True is considered 1 and False is 0.
fitness_comparison_sum = sum(fitness_comparison)
# Check if more than or equal to 50% of the objectives have fitness greater than the average.
# If True, then use the first percentage.
# If False, use the second percentage.
if fitness_comparison_sum >= len(fitness_comparison)/2:
adaptive_mutation_probability = self.mutation_probability[0]
else:
adaptive_mutation_probability = self.mutation_probability[1]

probs = numpy.random.random(size=offspring.shape[1])
for gene_idx in range(offspring.shape[1]):
Expand Down
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