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Support of NSGA-II
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@ahmedfgad
ahmedfgad committed Sep 2, 2023
1 parent 912a1d1 commit 0ea3674
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62 changes: 0 additions & 62 deletions NSGA-II/non_dominant_sorting.py
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120 changes: 83 additions & 37 deletions NSGA-II/non_dominant_sorting_crowding_distance.py
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Original file line number Diff line number Diff line change
@@ -1,13 +1,31 @@
import numpy

population_fitness = numpy.array([[20, 2.2],
[60, 4.4],
[65, 3.5],
[15, 4.4],
[55, 4.5],
[50, 1.8],
[80, 4.0],
[25, 4.6]])
fitness = numpy.array([[20, 2.2],
[60, 4.4],
[65, 3.5],
[15, 4.4],
[55, 4.5],
[50, 1.8],
[80, 4.0],
[25, 4.6]])

# fitness = numpy.array([20,
# 60,
# 65,
# 15,
# 55,
# 50,
# 80,
# 25])

# fitness = numpy.array([[20],
# [60],
# [65],
# [15],
# [55],
# [50],
# [80],
# [25]])

def get_non_dominated_set(curr_solutions):
"""
Expand Down Expand Up @@ -66,13 +84,13 @@ def get_non_dominated_set(curr_solutions):
# Return the dominated and non-dominated sets.
return dominated_set, non_dominated_set

def non_dominated_sorting(population_fitness):
def non_dominated_sorting(fitness):
"""
Apply the non-dominant sorting over the population_fitness to create the pareto fronts based on non-dominaned sorting of the solutions.
Apply the non-dominant sorting over the fitness to create the pareto fronts based on non-dominaned sorting of the solutions.
Parameters
----------
population_fitness : TYPE
fitness : TYPE
An array of the population fitness across all objective function.
Returns
Expand All @@ -87,15 +105,15 @@ def non_dominated_sorting(population_fitness):
# The remaining set to be explored for non-dominance.
# Initially it is set to the entire population.
# The solutions of each non-dominated set are removed after each iteration.
remaining_set = population_fitness.copy()
remaining_set = fitness.copy()

# Zipping the solution index with the solution's fitness.
# This helps to easily identify the index of each solution.
# Each element has:
# 1) The index of the solution.
# 2) An array of the fitness values of this solution across all objectives.
# remaining_set = numpy.array(list(zip(range(0, population_fitness.shape[0]), non_dominated_set)))
remaining_set = list(zip(range(0, population_fitness.shape[0]), remaining_set))
# remaining_set = numpy.array(list(zip(range(0, fitness.shape[0]), non_dominated_set)))
remaining_set = list(zip(range(0, fitness.shape[0]), remaining_set))

# A list mapping the index of each pareto front to the set of solutions in this front.
solutions_fronts_indices = [-1]*len(remaining_set)
Expand All @@ -116,14 +134,16 @@ def non_dominated_sorting(population_fitness):

return pareto_fronts, solutions_fronts_indices

def crowding_distance(pareto_front):
def crowding_distance(pareto_front, fitness):
"""
Calculate the crowding dstance for all solutions in the current pareto front.
Parameters
----------
pareto_front : TYPE
The set of solutions in the current pareto front.
fitness : TYPE
The fitness of the current population.
Returns
-------
Expand Down Expand Up @@ -164,8 +184,8 @@ def crowding_distance(pareto_front):
obj_sorted = sorted(obj, key=lambda x: x[1])

# Get the minimum and maximum values for the current objective.
obj_min_val = min(population_fitness[:, obj_idx])
obj_max_val = max(population_fitness[:, obj_idx])
obj_min_val = min(fitness[:, obj_idx])
obj_max_val = max(fitness[:, obj_idx])
denominator = obj_max_val - obj_min_val
# To avoid division by zero, set the denominator to a tiny value.
if denominator == 0:
Expand Down Expand Up @@ -217,9 +237,11 @@ def crowding_distance(pareto_front):
return obj_crowding_dist_list, crowding_dist_sum, crowding_dist_front_sorted_indices, crowding_dist_pop_sorted_indices

def tournament_selection_nsga2(self,
pareto_fronts,
solutions_fronts_indices,
num_parents):
fitness,
num_parents
# pareto_fronts,
# solutions_fronts_indices,
):

"""
Select the parents using the tournament selection technique for NSGA-II.
Expand All @@ -231,9 +253,10 @@ def tournament_selection_nsga2(self,
Later, the selected parents will mate to produce the offspring.
It accepts 2 parameters:
-fitness: The fitness values for the current population.
-num_parents: The number of parents to be selected.
-pareto_fronts: A nested array of all the pareto fronts. Each front has its solutions.
-solutions_fronts_indices: A list of the pareto front index of each solution in the current population.
-num_parents: The number of parents to be selected.
It returns an array of the selected parents alongside their indices in the population.
"""
Expand All @@ -246,6 +269,11 @@ def tournament_selection_nsga2(self,
# The indices of the selected parents.
parents_indices = []

# TODO If there is only a single objective, each pareto front is expected to have only 1 solution.
# TODO Make a test to check for that behaviour.
# Find the pareto fronts and the solutions' indicies in each front.
pareto_fronts, solutions_fronts_indices = non_dominated_sorting(fitness)

# Randomly generate pairs of indices to apply for NSGA-II tournament selection for selecting the parents solutions.
rand_indices = numpy.random.randint(low=0.0,
high=len(solutions_fronts_indices),
Expand Down Expand Up @@ -284,7 +312,8 @@ def tournament_selection_nsga2(self,
# Reaching here means the pareto front has more than 1 solution.

# Calculate the crowding distance of the solutions of the pareto front.
obj_crowding_distance_list, crowding_distance_sum, crowding_dist_front_sorted_indices, crowding_dist_pop_sorted_indices = crowding_distance(pareto_front.copy())
obj_crowding_distance_list, crowding_distance_sum, crowding_dist_front_sorted_indices, crowding_dist_pop_sorted_indices = crowding_distance(pareto_front=pareto_front.copy(),
fitness=fitness)

# This list has the sorted front-based indices for the solutions in the current pareto front.
crowding_dist_front_sorted_indices = list(crowding_dist_front_sorted_indices)
Expand Down Expand Up @@ -333,9 +362,11 @@ def tournament_selection_nsga2(self,
return parents, numpy.array(parents_indices)

def nsga2_selection(self,
pareto_fronts,
solutions_fronts_indices,
num_parents):
fitness,
num_parents
# pareto_fronts,
# solutions_fronts_indices
):

"""
Select the parents using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II).
Expand All @@ -348,9 +379,10 @@ def nsga2_selection(self,
Later, the selected parents will mate to produce the offspring.
It accepts 2 parameters:
-fitness: The fitness values for the current population.
-num_parents: The number of parents to be selected.
-pareto_fronts: A nested array of all the pareto fronts. Each front has its solutions.
-solutions_fronts_indices: A list of the pareto front index of each solution in the current population.
-num_parents: The number of parents to be selected.
It returns an array of the selected parents alongside their indices in the population.
"""
Expand All @@ -363,6 +395,11 @@ def nsga2_selection(self,
# The indices of the selected parents.
parents_indices = []

# TODO If there is only a single objective, each pareto front is expected to have only 1 solution.
# TODO Make a test to check for that behaviour.
# Find the pareto fronts and the solutions' indicies in each front.
pareto_fronts, solutions_fronts_indices = non_dominated_sorting(fitness)

# The number of remaining parents to be selected.
num_remaining_parents = num_parents

Expand All @@ -387,7 +424,8 @@ def nsga2_selection(self,
# If only a subset of the front is needed, then use the crowding distance to sort the solutions and select only the number needed.

# Calculate the crowding distance of the solutions of the pareto front.
obj_crowding_distance_list, crowding_distance_sum, crowding_dist_front_sorted_indices, crowding_dist_pop_sorted_indices = crowding_distance(current_pareto_front.copy())
obj_crowding_distance_list, crowding_distance_sum, crowding_dist_front_sorted_indices, crowding_dist_pop_sorted_indices = crowding_distance(pareto_front=current_pareto_front.copy(),
fitness=fitness)

for selected_solution_idx in crowding_dist_pop_sorted_indices[0:num_remaining_parents]:
# Insert the parent into the parents array.
Expand All @@ -404,8 +442,10 @@ def nsga2_selection(self,
# Make sure the parents indices is returned as a NumPy array.
return parents, numpy.array(parents_indices)


pareto_fronts, solutions_fronts_indices = non_dominated_sorting(population_fitness)
# TODO If there is only a single objective, each pareto front is expected to have only 1 solution.
# TODO Make a test to check for that behaviour.
# Find the pareto fronts and the solutions' indicies in each front.
pareto_fronts, solutions_fronts_indices = non_dominated_sorting(fitness)
# # print('\nsolutions_fronts_indices\n', solutions_fronts_indices)
# for i, s in enumerate(pareto_fronts):
# # print(f'Dominated Pareto Front Set {i+1}:\n{s}')
Expand All @@ -422,27 +462,33 @@ class Object(object):
obj.K_tournament = 2

parents, parents_indices = tournament_selection_nsga2(self=obj,
pareto_fronts=pareto_fronts,
solutions_fronts_indices=solutions_fronts_indices,
num_parents=40)
fitness=fitness,
num_parents=4
# pareto_fronts=pareto_fronts,
# solutions_fronts_indices=solutions_fronts_indices,
)
print(f'Tournament Parent Selection for NSGA-II - Indices: \n{parents_indices}')

parents, parents_indices = nsga2_selection(self=obj,
pareto_fronts=pareto_fronts,
solutions_fronts_indices=solutions_fronts_indices,
num_parents=40)
fitness=fitness,
num_parents=4
# pareto_fronts=pareto_fronts,
# solutions_fronts_indices=solutions_fronts_indices,
)
print(f'NSGA-II Parent Selection - Indices: \n{parents_indices}')

# for idx in range(len(pareto_fronts)):
# # Fetch the current pareto front.
# pareto_front = pareto_fronts[idx]
# obj_crowding_distance_list, crowding_distance_sum, crowding_dist_front_sorted_indices, crowding_dist_pop_sorted_indices = crowding_distance(pareto_front.copy())
# obj_crowding_distance_list, crowding_distance_sum, crowding_dist_front_sorted_indices, crowding_dist_pop_sorted_indices = crowding_distance(pareto_front=pareto_front.copy(),
# fitness=fitness)
# print('Front IDX', crowding_dist_front_sorted_indices)
# print('POP IDX ', crowding_dist_pop_sorted_indices)
# print(f'Sorted Sum of Crowd Dists\n{crowding_distance_sum}')

# # Fetch the current pareto front.
# pareto_front = pareto_fronts[0]
# obj_crowding_distance_list, crowding_distance_sum, crowding_dist_front_sorted_indices, crowding_dist_pop_sorted_indices = crowding_distance(pareto_front.copy())
# obj_crowding_distance_list, crowding_distance_sum, crowding_dist_front_sorted_indices, crowding_dist_pop_sorted_indices = crowding_distance(pareto_front=pareto_front.copy(),
# fitness=fitness)
# print('\n', crowding_dist_pop_sorted_indices)
# print(f'Sorted Sum of Crowd Dists\n{crowding_distance_sum}')
2 changes: 1 addition & 1 deletion pygad/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,3 @@
from .pygad import * # Relative import.

__version__ = "3.1.1"
__version__ = "3.2.0"
4 changes: 2 additions & 2 deletions pygad/helper/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,3 @@
from pygad.helper import unique
from pygad.helper import unique, nsga2

__version__ = "1.1.0"
__version__ = "1.2.0"
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