Release Date: 15 April 2020

1. The pygad.GA class accepts a new argument named fitness_func which accepts a function to be used for calculating the fitness values for the solutions. This allows the project to be customized to any problem by building the right fitness function.

Release Date: 4 May 2020

1. The pygad.GA attributes are moved from the class scope to the instance scope.
2. Raising an exception for incorrect values of the passed parameters.
3. Two new parameters are added to the pygad.GA class constructor (init_range_low and init_range_high) allowing the user to customize the range from which the genes values in the initial population are selected.
4. The code object __code__ of the passed fitness function is checked to ensure it has the right number of parameters.

Release Date: 13 May 2020

1. The fitness function accepts a new argument named sol_idx representing the index of the solution within the population.
2. A new parameter to the pygad.GA class constructor named initial_population is supported to allow the user to use a custom initial population to be used by the genetic algorithm. If not None, then the passed population will be used. If None, then the genetic algorithm will create the initial population using the sol_per_pop and num_genes parameters.
3. The parameters sol_per_pop and num_genes are optional and set to None by default.
4. A new parameter named callback_generation is introduced in the pygad.GA class constructor. It accepts a function with a single parameter representing the pygad.GA class instance. This function is called after each generation. This helps the user to do post-processing or debugging operations after each generation.

Release Date: 14 May 2020

1. The best_solution() method in the pygad.GA class returns a new output representing the index of the best solution within the population. Now, it returns a total of 3 outputs and their order is: best solution, best solution fitness, and best solution index. Here is an example:

solution, solution_fitness, solution_idx = ga_instance.best_solution()
print("Parameters of the best solution :", solution)
print("Fitness value of the best solution :", solution_fitness, "\n")
print("Index of the best solution :", solution_idx, "\n")

2. A new attribute named best_solution_generation is added to the instances of the pygad.GA class. it holds the generation number at which the best solution is reached. It is only assigned the generation number after the run() method completes. Otherwise, its value is -1. Example:

print("Best solution reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation))

3. The best_solution_fitness attribute is renamed to best_solutions_fitness (plural solution).
4. Mutation is applied independently for the genes.

Release Date: 17 May 2020

1. Adding 2 extra modules (pygad.nn and pygad.gann) for building and training neural networks with the genetic algorithm.

Release Date: 18 May 2020

1. The initial value of the generations_completed attribute of instances from the pygad.GA class is 0 rather than None.

2. An optional bool parameter named mutation_by_replacement is added to the constructor of the pygad.GA class. It works only when the selected type of mutation is random (mutation_type="random"). In this case, setting mutation_by_replacement=True means replace the gene by the randomly generated value. If False, then it has no effect and random mutation works by adding the random value to the gene. This parameter should be used when the gene falls within a fixed range and its value must not go out of this range. Here are some examples:

Assume there is a gene with the value 0.5.

If mutation_type="random" and mutation_by_replacement=False, then the generated random value (e.g. 0.1) will be added to the gene value. The new gene value is 0.5+0.1=0.6.

If mutation_type="random" and mutation_by_replacement=True, then the generated random value (e.g. 0.1) will replace the gene value. The new gene value is 0.1.

3. None value could be assigned to the mutation_type and crossover_type parameters of the pygad.GA class constructor. When None, this means the step is bypassed and has no action.

Release date: 1 June 2020

1. A new module named pygad.cnn is supported for building convolutional neural networks.
2. A new module named pygad.gacnn is supported for training convolutional neural networks using the genetic algorithm.
3. The pygad.plot_result() method has 3 optional parameters named title, xlabel, and ylabel to customize the plot title, x-axis label, and y-axis label, respectively.
4. The pygad.nn module supports the softmax activation function.
5. The name of the pygad.nn.predict_outputs() function is changed to pygad.nn.predict().
6. The name of the pygad.nn.train_network() function is changed to pygad.nn.train().

Release date: 5 July 2020

1. A new parameter named delay_after_gen is added which accepts a non-negative number specifying the time in seconds to wait after a generation completes and before going to the next generation. It defaults to 0.0 which means no delay after the generation.

2. The passed function to the callback_generation parameter of the pygad.GA class constructor can terminate the execution of the genetic algorithm if it returns the string stop. This causes the run() method to stop.

One important use case for that feature is to stop the genetic algorithm when a condition is met before passing though all the generations. The user may assigned a value of 100 to the num_generations parameter of the pygad.GA class constructor. Assuming that at generation 50, for example, a condition is met and the user wants to stop the execution before waiting the remaining 50 generations. To do that, just make the function passed to the callback_generation parameter to return the string stop.

Here is an example of a function to be passed to the callback_generation parameter which stops the execution if the fitness value 70 is reached. The value 70 might be the best possible fitness value. After being reached, then there is no need to pass through more generations because no further improvement is possible.

def func_generation(ga_instance):
 if ga_instance.best_solution()[1] >= 70:
     return "stop"

Release date: 19 July 2020

1. 2 new optional parameters added to the constructor of the pygad.GA class which are crossover_probability and mutation_probability. While applying the crossover operation, each parent has a random value generated between 0.0 and 1.0. If this random value is less than or equal to the value assigned to the crossover_probability parameter, then the parent is selected for the crossover operation. For the mutation operation, a random value between 0.0 and 1.0 is generated for each gene in the solution. If this value is less than or equal to the value assigned to the mutation_probability, then this gene is selected for mutation.
2. A new optional parameter named linewidth is added to the plot_result() method to specify the width of the curve in the plot. It defaults to 3.0.
3. Previously, the indices of the genes selected for mutation was randomly generated once for all solutions within the generation. Currently, the genes' indices are randomly generated for each solution in the population. If the population has 4 solutions, the indices are randomly generated 4 times inside the single generation, 1 time for each solution.
4. Previously, the position of the point(s) for the single-point and two-points crossover was(were) randomly selected once for all solutions within the generation. Currently, the position(s) is(are) randomly selected for each solution in the population. If the population has 4 solutions, the position(s) is(are) randomly generated 4 times inside the single generation, 1 time for each solution.
5. A new optional parameter named gene_space as added to the pygad.GA class constructor. It is used to specify the possible values for each gene in case the user wants to restrict the gene values. It is useful if the gene space is restricted to a certain range or to discrete values. For more information, check the More about the gene_space Parameter section. Thanks to Prof. Tamer A. Farrag for requesting this useful feature.

Release Date: 6 August 2020

1. A bug fix in assigning the value to the initial_population parameter.
2. A new parameter named gene_type is added to control the gene type. It can be either int or float. It has an effect only when the parameter gene_space is None.
3. 7 new parameters that accept callback functions: on_start, on_fitness, on_parents, on_crossover, on_mutation, on_generation, and on_stop.

Release Date: 11 September 2020

1. The learning_rate parameter in the pygad.nn.train() function defaults to 0.01.
2. Added support of building neural networks for regression using the new parameter named problem_type. It is added as a parameter to both pygad.nn.train() and pygad.nn.predict() functions. The value of this parameter can be either classification or regression to define the problem type. It defaults to classification.
3. The activation function for a layer can be set to the string "None" to refer that there is no activation function at this layer. As a result, the supported values for the activation function are "sigmoid", "relu", "softmax", and "None".

To build a regression network using the pygad.nn module, just do the following:

1. Set the problem_type parameter in the pygad.nn.train() and pygad.nn.predict() functions to the string "regression".
2. Set the activation function for the output layer to the string "None". This sets no limits on the range of the outputs as it will be from -infinity to +infinity. If you are sure that all outputs will be nonnegative values, then use the ReLU function.

Check the documentation of the pygad.nn module for an example that builds a neural network for regression. The regression example is also available at this GitHub project: https://github.com/ahmedfgad/NumPyANN

To build and train a regression network using the pygad.gann module, do the following:

1. Set the problem_type parameter in the pygad.nn.train() and pygad.nn.predict() functions to the string "regression".
2. Set the output_activation parameter in the constructor of the pygad.gann.GANN class to "None".

Check the documentation of the pygad.gann module for an example that builds and trains a neural network for regression. The regression example is also available at this GitHub project: https://github.com/ahmedfgad/NeuralGenetic

To build a classification network, either ignore the problem_type parameter or set it to "classification" (default value). In this case, the activation function of the last layer can be set to any type (e.g. softmax).

Release Date: 11 September 2020

1. A bug fix when the problem_type argument is set to regression.

Release Date: 14 September 2020

1. Bug fix to support building and training regression neural networks with multiple outputs.

Release Date: 20 September 2020

1. Support of a new module named kerasga so that the Keras models can be trained by the genetic algorithm using PyGAD.

Release Date: 3 October 2020

1. Bug fix in applying the crossover operation when the crossover_probability parameter is used. Thanks to Eng. Hamada Kassem, Research and Teaching Assistant, Construction Engineering and Management, Faculty of Engineering, Alexandria University, Egypt.

Release Date: 06 December 2020

1. The fitness values of the initial population are considered in the best_solutions_fitness attribute.
2. An optional parameter named save_best_solutions is added. It defaults to False. When it is True, then the best solution after each generation is saved into an attribute named best_solutions. If False, then no solutions are saved and the best_solutions attribute will be empty.
3. Scattered crossover is supported. To use it, assign the crossover_type parameter the value "scattered".
4. NumPy arrays are now supported by the gene_space parameter.
5. The following parameters (gene_type, crossover_probability, mutation_probability, delay_after_gen) can be assigned to a numeric value of any of these data types: int, float, numpy.int, numpy.int8, numpy.int16, numpy.int32, numpy.int64, numpy.float, numpy.float16, numpy.float32, or numpy.float64.

Release Date: 03 January 2021

1. Support of a new module pygad.torchga to train PyTorch models using PyGAD. Check its documentation.
2. Support of adaptive mutation where the mutation rate is determined by the fitness value of each solution. Read the Adaptive Mutation section for more details. Also, read this paper: Libelli, S. Marsili, and P. Alba. "Adaptive mutation in genetic algorithms." Soft computing 4.2 (2000): 76-80.
3. Before the run() method completes or exits, the fitness value of the best solution in the current population is appended to the best_solution_fitness list attribute. Note that the fitness value of the best solution in the initial population is already saved at the beginning of the list. So, the fitness value of the best solution is saved before the genetic algorithm starts and after it ends.
4. When the parameter parent_selection_type is set to sss (steady-state selection), then a warning message is printed if the value of the keep_parents parameter is set to 0.
5. More validations to the user input parameters.
6. The default value of the mutation_percent_genes is set to the string "default" rather than the integer 10. This change helps to know whether the user explicitly passed a value to the mutation_percent_genes parameter or it is left to its default one. The "default" value is later translated into the integer 10.
7. The mutation_percent_genes parameter is no longer accepting the value 0. It must be >0 and <=100.
8. The built-in warnings module is used to show warning messages rather than just using the print() function.
9. A new bool parameter called suppress_warnings is added to the constructor of the pygad.GA class. It allows the user to control whether the warning messages are printed or not. It defaults to False which means the messages are printed.
10. A helper method called adaptive_mutation_population_fitness() is created to calculate the average fitness value used in adaptive mutation to filter the solutions.
11. The best_solution() method accepts a new optional parameter called pop_fitness. It accepts a list of the fitness values of the solutions in the population. If None, then the cal_pop_fitness() method is called to calculate the fitness values of the population.

Release Date: 10 January 2021

1. In the gene_space parameter, any None value (regardless of its index or axis), is replaced by a randomly generated number based on the 3 parameters init_range_low, init_range_high, and gene_type. So, the None value in [..., None, ...] or [..., [..., None, ...], ...] are replaced with random values. This gives more freedom in building the space of values for the genes.
2. All the numbers passed to the gene_space parameter are casted to the type specified in the gene_type parameter.
3. The numpy.uint data type is supported for the parameters that accept integer values.
4. In the pygad.kerasga module, the model_weights_as_vector() function uses the trainable attribute of the model's layers to only return the trainable weights in the network. So, only the trainable layers with their trainable attribute set to True (trainable=True), which is the default value, have their weights evolved. All non-trainable layers with the trainable attribute set to False (trainable=False) will not be evolved. Thanks to Prof. Tamer A. Farrag for pointing about that at GitHub.

Release Date: 15 January 2021

1. A bug fix when save_best_solutions=True. Refer to this issue for more information: #25

Release Date: 16 February 2021

1. In the gene_space argument, the user can use a dictionary to specify the lower and upper limits of the gene. This dictionary must have only 2 items with keys low and high to specify the low and high limits of the gene, respectively. This way, PyGAD takes care of not exceeding the value limits of the gene. For a problem with only 2 genes, then using gene_space=[{'low': 1, 'high': 5}, {'low': 0.2, 'high': 0.81}] means the accepted values in the first gene start from 1 (inclusive) to 5 (exclusive) while the second one has values between 0.2 (inclusive) and 0.85 (exclusive). For more information, please check the Limit the Gene Value Range section of the documentation.
2. The plot_result() method returns the figure so that the user can save it.
3. Bug fixes in copying elements from the gene space.
4. For a gene with a set of discrete values (more than 1 value) in the gene_space parameter like [0, 1], it was possible that the gene value may not change after mutation. That is if the current value is 0, then the randomly selected value could also be 0. Now, it is verified that the new value is changed. So, if the current value is 0, then the new value after mutation will not be 0 but 1.

Release Date: 20 February 2021

1. 4 new instance attributes are added to hold temporary results after each generation: last_generation_fitness holds the fitness values of the solutions in the last generation, last_generation_parents holds the parents selected from the last generation, last_generation_offspring_crossover holds the offspring generated after applying the crossover in the last generation, and last_generation_offspring_mutation holds the offspring generated after applying the mutation in the last generation. You can access these attributes inside the on_generation() method for example.
2. A bug fixed when the initial_population parameter is used. The bug occurred due to a mismatch between the data type of the array assigned to initial_population and the gene type in the gene_type attribute. Assuming that the array assigned to the initial_population parameter is ((1, 1), (3, 3), (5, 5), (7, 7)) which has type int. When gene_type is set to float, then the genes will not be float but casted to int because the defined array has int type. The bug is fixed by forcing the array assigned to initial_population to have the data type in the gene_type attribute. Check the issue at GitHub: #27

Thanks to Andrei Rozanski [PhD Bioinformatics Specialist, Department of Tissue Dynamics and Regeneration, Max Planck Institute for Biophysical Chemistry, Germany] for opening my eye to the first change.

Thanks to Marios Giouvanakis, a PhD candidate in Electrical & Computer Engineer, Aristotle University of Thessaloniki (Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης), Greece, for emailing me about the second issue.

Release Date: 12 March 2021

1. A new bool parameter called allow_duplicate_genes is supported. If True, which is the default, then a solution/chromosome may have duplicate gene values. If False, then each gene will have a unique value in its solution. Check the Prevent Duplicates in Gene Values section for more details.
2. The last_generation_fitness is updated at the end of each generation not at the beginning. This keeps the fitness values of the most up-to-date population assigned to the last_generation_fitness parameter.

PyGAD 2.14.0 has an issue that is solved in PyGAD 2.14.1. Please consider using 2.14.1 not 2.14.0.

Release Date: 19 May 2021

1. Issue #40 is solved. Now, the None value works with the crossover_type and mutation_type parameters: #40
2. The gene_type parameter supports accepting a list/tuple/numpy.ndarray of numeric data types for the genes. This helps to control the data type of each individual gene. Previously, the gene_type can be assigned only to a single data type that is applied for all genes. For more information, check the More about the gene_type Parameter section. Thanks to Rainer Engel for asking about this feature in this discussion: #43
3. A new bool attribute named gene_type_single is added to the pygad.GA class. It is True when there is a single data type assigned to the gene_type parameter. When the gene_type parameter is assigned a list/tuple/numpy.ndarray, then gene_type_single is set to False.
4. The mutation_by_replacement flag now has no effect if gene_space exists except for the genes with None values. For example, for gene_space=[None, [5, 6]] the mutation_by_replacement flag affects only the first gene which has None for its value space.
5. When an element has a value of None in the gene_space parameter (e.g. gene_space=[None, [5, 6]]), then its value will be randomly generated for each solution rather than being generate once for all solutions. Previously, the gene with None value in gene_space is the same across all solutions
6. Some changes in the documentation according to issue #32: #32

Release Date: 27 May 2021

1. Some bug fixes when the gene_type parameter is nested. Thanks to Rainer Engel for opening a discussion to report this bug: #43 (reply in thread)

Rainer Engel helped a lot in suggesting new features and suggesting enhancements in 2.14.0 to 2.14.2 releases.

Release Date: 6 June 2021

1. Some bug fixes when setting the save_best_solutions parameter to True. Previously, the best solution for generation i was added into the best_solutions attribute at generation i+1. Now, the best_solutions attribute is updated by each best solution at its exact generation.

Release Date: 17 June 2021

1. Control the precision of all genes/individual genes. Thanks to Rainer for asking about this feature: #43 (comment)
2. A new attribute named last_generation_parents_indices holds the indices of the selected parents in the last generation.
3. In adaptive mutation, no need to recalculate the fitness values of the parents selected in the last generation as these values can be returned based on the last_generation_fitness and last_generation_parents_indices attributes. This speeds-up the adaptive mutation.
4. When a sublist has a value of None in the gene_space parameter (e.g. gene_space=[[1, 2, 3], [5, 6, None]]), then its value will be randomly generated for each solution rather than being generated once for all solutions. Previously, a value of None in a sublist of the gene_space parameter was identical across all solutions.
5. The dictionary assigned to the gene_space parameter itself or one of its elements has a new key called "step" to specify the step of moving from the start to the end of the range specified by the 2 existing keys "low" and "high". An example is {"low": 0, "high": 30, "step": 2} to have only even values for the gene(s) starting from 0 to 30. For more information, check the More about the gene_space Parameter section. #48
6. A new function called predict() is added in both the pygad.kerasga and pygad.torchga modules to make predictions. This makes it easier than using custom code each time a prediction is to be made.
7. A new parameter called stop_criteria allows the user to specify one or more stop criteria to stop the evolution based on some conditions. Each criterion is passed as str which has a stop word. The current 2 supported words are reach and saturate. reach stops the run() method if the fitness value is equal to or greater than a given fitness value. An example for reach is "reach_40" which stops the evolution if the fitness is >= 40. saturate means stop the evolution if the fitness saturates for a given number of consecutive generations. An example for saturate is "saturate_7" which means stop the run() method if the fitness does not change for 7 consecutive generations. Thanks to Rainer for asking about this feature: #44
8. A new bool parameter, defaults to False, named save_solutions is added to the constructor of the pygad.GA class. If True, then all solutions in each generation are appended into an attribute called solutions which is NumPy array.
9. The plot_result() method is renamed to plot_fitness(). The users should migrate to the new name as the old name will be removed in the future.
10. Four new optional parameters are added to the plot_fitness() function in the pygad.GA class which are font_size=14, save_dir=None, color="#3870FF", and plot_type="plot". Use font_size to change the font of the plot title and labels. save_dir accepts the directory to which the figure is saved. It defaults to None which means do not save the figure. color changes the color of the plot. plot_type changes the plot type which can be either "plot" (default), "scatter", or "bar". #47
11. The default value of the title parameter in the plot_fitness() method is "PyGAD - Generation vs. Fitness" rather than "PyGAD - Iteration vs. Fitness".
12. A new method named plot_new_solution_rate() creates, shows, and returns a figure showing the rate of new/unique solutions explored in each generation. It accepts the same parameters as in the plot_fitness() method. This method only works when save_solutions=True in the pygad.GA class's constructor.
13. A new method named plot_genes() creates, shows, and returns a figure to show how each gene changes per each generation. It accepts similar parameters like the plot_fitness() method in addition to the graph_type, fill_color, and solutions parameters. The graph_type parameter can be either "plot" (default), "boxplot", or "histogram". fill_color accepts the fill color which works when graph_type is either "boxplot" or "histogram". solutions can be either "all" or "best" to decide whether all solutions or only best solutions are used.
14. The gene_type parameter now supports controlling the precision of float data types. For a gene, rather than assigning just the data type like float, assign a list/tuple/numpy.ndarray with 2 elements where the first one is the type and the second one is the precision. For example, [float, 2] forces a gene with a value like 0.1234 to be 0.12. For more information, check the More about the gene_type Parameter section.

Release Date: 18 June 2021

1. Fix a bug when keep_parents is set to a positive integer. #49

Release Date: 18 June 2021

1. Fix a bug when using the kerasga or torchga modules. #51

Release Date: 19 June 2021

1. A user-defined function can be passed to the mutation_type, crossover_type, and parent_selection_type parameters in the pygad.GA class to create a custom mutation, crossover, and parent selection operators. Check the User-Defined Crossover, Mutation, and Parent Selection Operators section for more details. #50

Release Date: 28 September 2021

1. The user can use the tqdm library to show a progress bar. #50.

import pygad
import numpy
import tqdm

equation_inputs = [4,-2,3.5]
desired_output = 44

def fitness_func(ga_instance, solution, solution_idx):
    output = numpy.sum(solution * equation_inputs)
    fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
    return fitness

num_generations = 10000
with tqdm.tqdm(total=num_generations) as pbar:
    ga_instance = pygad.GA(num_generations=num_generations,
                           sol_per_pop=5,
                           num_parents_mating=2,
                           num_genes=len(equation_inputs),
                           fitness_func=fitness_func,
                           on_generation=lambda _: pbar.update(1))
    
    ga_instance.run()

ga_instance.plot_result()

But this work does not work if the ga_instance will be pickled (i.e. the save() method will be called.

ga_instance.save("test")

To solve this issue, define a function and pass it to the on_generation parameter. In the next code, the on_generation_progress() function is defined which updates the progress bar.

import pygad
import numpy
import tqdm

equation_inputs = [4,-2,3.5]
desired_output = 44

def fitness_func(ga_instance, solution, solution_idx):
    output = numpy.sum(solution * equation_inputs)
    fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
    return fitness

def on_generation_progress(ga):
    pbar.update(1)

num_generations = 100
with tqdm.tqdm(total=num_generations) as pbar:
    ga_instance = pygad.GA(num_generations=num_generations,
                           sol_per_pop=5,
                           num_parents_mating=2,
                           num_genes=len(equation_inputs),
                           fitness_func=fitness_func,
                           on_generation=on_generation_progress)

    ga_instance.run()

ga_instance.plot_result()

ga_instance.save("test")

2. Solved the issue of unequal length between the solutions and solutions_fitness when the save_solutions parameter is set to True. Now, the fitness of the last population is appended to the solutions_fitness array. #64

3. There was an issue of getting the length of these 4 variables (solutions, solutions_fitness, best_solutions, and best_solutions_fitness) doubled after each call of the run() method. This is solved by resetting these variables at the beginning of the run() method. #62

4. Bug fixes when adaptive mutation is used (mutation_type="adaptive"). #65

Release Date: 2 February 2022

1. A new instance attribute called previous_generation_fitness added in the pygad.GA class. It holds the fitness values of one generation before the fitness values saved in the last_generation_fitness.
2. Issue in the cal_pop_fitness() method in getting the correct indices of the previous parents. This is solved by using the previous generation's fitness saved in the new attribute previous_generation_fitness to return the parents' fitness values. Thanks to Tobias Tischhauser (M.Sc. - Mitarbeiter Institut EMS, Departement Technik, OST – Ostschweizer Fachhochschule, Switzerland) for detecting this bug.

Release Date: 2 February 2022

1. Validate the fitness value returned from the fitness function. An exception is raised if something is wrong. #67

Release Date: 8 July 2022

1. An issue is solved when the gene_space parameter is given a fixed value. e.g. gene_space=[range(5), 4]. The second gene's value is static (4) which causes an exception.
2. Fixed the issue where the allow_duplicate_genes parameter did not work when mutation is disabled (i.e. mutation_type=None). This is by checking for duplicates after crossover directly. #39
3. Solve an issue in the tournament_selection() method as the indices of the selected parents were incorrect. #89
4. Reuse the fitness values of the previously explored solutions rather than recalculating them. This feature only works if save_solutions=True.
5. Parallel processing is supported. This is by the introduction of a new parameter named parallel_processing in the constructor of the pygad.GA class. Thanks to @windowshopr for opening the issue #78 at GitHub. Check the Parallel Processing in PyGAD section for more information and examples.

Release Date: 9 September 2022

1. Raise an exception if the sum of fitness values is zero while either roulette wheel or stochastic universal parent selection is used. #129
2. Initialize the value of the run_completed property to False. #122
3. The values of these properties are no longer reset with each call to the run() method self.best_solutions, self.best_solutions_fitness, self.solutions, self.solutions_fitness: #123. Now, the user can have the flexibility of calling the run() method more than once while extending the data collected after each generation. Another advantage happens when the instance is loaded and the run() method is called, as the old fitness value are shown on the graph alongside with the new fitness values. Read more in this section: Continue without Losing Progress
4. Thanks Prof. Fernando Jiménez Barrionuevo (Dept. of Information and Communications Engineering, University of Murcia, Murcia, Spain) for editing this comment in the code. https://github.com/ahmedfgad/GeneticAlgorithmPython/commit/5315bbec02777df96ce1ec665c94dece81c440f4
5. A bug fixed when crossover_type=None.
6. Support of elitism selection through a new parameter named keep_elitism. It defaults to 1 which means for each generation keep only the best solution in the next generation. If assigned 0, then it has no effect. Read more in this section: Elitism Selection. #74
7. A new instance attribute named last_generation_elitism added to hold the elitism in the last generation.
8. A new parameter called random_seed added to accept a seed for the random function generators. Credit to this issue #70 and Prof. Fernando Jiménez Barrionuevo. Read more in this section: Random Seed.
9. Editing the pygad.TorchGA module to make sure the tensor data is moved from GPU to CPU. Thanks to Rasmus Johansson for opening this pull request: ahmedfgad/TorchGA#2

Release Date: 19 September 2022

1. A big fix when keep_elitism is used. #132

Release Date: 14 February 2023

1. Remove numpy.int and numpy.float from the list of supported data types. #151 #152
2. Call the on_crossover() callback function even if crossover_type is None. #138
3. Call the on_mutation() callback function even if mutation_type is None. #138

Release Date: 14 February 2023

1. Bug fixes.

Release Date: 22 February 2023

1. A new summary() method is supported to return a Keras-like summary of the PyGAD lifecycle.
2. A new optional parameter called fitness_batch_size is supported to calculate the fitness in batches. If it is assigned the value 1 or None (default), then the normal flow is used where the fitness function is called for each individual solution. If the fitness_batch_size parameter is assigned a value satisfying this condition 1 < fitness_batch_size <= sol_per_pop, then the solutions are grouped into batches of size fitness_batch_size and the fitness function is called once for each batch. In this case, the fitness function must return a list/tuple/numpy.ndarray with a length equal to the number of solutions passed. #136.
3. The cloudpickle library (https://github.com/cloudpipe/cloudpickle) is used instead of the pickle library to pickle the pygad.GA objects. This solves the issue of having to redefine the functions (e.g. fitness function). The cloudpickle library is added as a dependency in the requirements.txt file. #159
4. Support of assigning methods to these parameters: fitness_func, crossover_type, mutation_type, parent_selection_type, on_start, on_fitness, on_parents, on_crossover, on_mutation, on_generation, and on_stop. #92 #138
5. Validating the output of the parent selection, crossover, and mutation functions.
6. The built-in parent selection operators return the parent's indices as a NumPy array.
7. The outputs of the parent selection, crossover, and mutation operators must be NumPy arrays.
8. Fix an issue when allow_duplicate_genes=True. #39
9. Fix an issue creating scatter plots of the solutions' fitness.
10. Sampling from a set() is no longer supported in Python 3.11. Instead, sampling happens from a list(). Thanks Marco Brenna for pointing to this issue.
11. The lifecycle is updated to reflect that the new population's fitness is calculated at the end of the lifecycle not at the beginning. #154 (comment)
12. There was an issue when save_solutions=True that causes the fitness function to be called for solutions already explored and have their fitness pre-calculated. #160
13. A new instance attribute named last_generation_elitism_indices added to hold the indices of the selected elitism. This attribute helps to re-use the fitness of the elitism instead of calling the fitness function.
14. Fewer calls to the best_solution() method which in turns saves some calls to the fitness function.
15. Some updates in the documentation to give more details about the cal_pop_fitness() method. #79 (comment)

Release Date: 22 February 2023

1. Add the cloudpickle library as a dependency.

Release Date 23 February 2023

1. Fix an issue when parallel processing was used where the elitism solutions' fitness values are not re-used. #160 (comment)

Release Date 8 April 2023

1. The structure of the library is changed and some methods defined in the pygad.py module are moved to the pygad.utils, pygad.helper, and pygad.visualize submodules.
2. The pygad.utils.parent_selection module has a class named ParentSelection where all the parent selection operators exist. The pygad.GA class extends this class.
3. The pygad.utils.crossover module has a class named Crossover where all the crossover operators exist. The pygad.GA class extends this class.
4. The pygad.utils.mutation module has a class named Mutation where all the mutation operators exist. The pygad.GA class extends this class.
5. The pygad.helper.unique module has a class named Unique some helper methods exist to solve duplicate genes and make sure every gene is unique. The pygad.GA class extends this class.
6. The pygad.visualize.plot module has a class named Plot where all the methods that create plots exist. The pygad.GA class extends this class.
7. Support of using the logging module to log the outputs to both the console and text file instead of using the print() function. This is by assigning the logging.Logger to the new logger parameter. Check the Logging Outputs for more information.
8. A new instance attribute called logger to save the logger.
9. The function/method passed to the fitness_func parameter accepts a new parameter that refers to the instance of the pygad.GA class. Check this for an example: Use Functions and Methods to Build Fitness Function and Callbacks. #163
10. Update the documentation to include an example of using functions and methods to calculate the fitness and build callbacks. Check this for more details: Use Functions and Methods to Build Fitness Function and Callbacks. #92 (comment)
11. Validate the value passed to the initial_population parameter.
12. Validate the type and length of the pop_fitness parameter of the best_solution() method.
13. Some edits in the documentation. #106
14. Fix an issue when building the initial population as (some) genes have their value taken from the mutation range (defined by the parameters random_mutation_min_val and random_mutation_max_val) instead of using the parameters init_range_low and init_range_high.
15. The summary() method returns the summary as a single-line string. Just log/print the returned string it to see it properly.
16. The callback_generation parameter is removed. Use the on_generation parameter instead.
17. There was an issue when using the parallel_processing parameter with Keras and PyTorch. As Keras/PyTorch are not thread-safe, the predict() method gives incorrect and weird results when more than 1 thread is used. #145 ahmedfgad/TorchGA#5 ahmedfgad/KerasGA#6. Thanks to this StackOverflow answer.
18. Replace numpy.float by float in the 2 parent selection operators roulette wheel and stochastic universal. #168

Release Date 20 April 2023

1. Fix an issue with passing user-defined function/method for parent selection. #179

Release Date 20 June 2023

1. Fix a bug when the initial population has duplciate genes if a nested gene space is used.
2. The gene_space parameter can no longer be assigned a tuple.
3. Fix a bug when the gene_space parameter has a member of type tuple.
4. A new instance attribute called gene_space_unpacked which has the unpacked gene_space. It is used to solve duplicates. For infinite ranges in the gene_space, they are unpacked to a limited number of values (e.g. 100).
5. Bug fixes when creating the initial population using gene_space attribute.
6. When a dict is used with the gene_space attribute, the new gene value was calculated by summing 2 values: 1) the value sampled from the dict 2) a random value returned from the random mutation range defined by the 2 parameters random_mutation_min_val and random_mutation_max_val. This might cause the gene value to exceed the range limit defined in the gene_space. To respect the gene_space range, this release only returns the value from the dict without summing it to a random value.
7. Formatting the strings using f-string instead of the format() method. #189
8. In the __init__() of the pygad.GA class, the logged error messages are handled using a try-except block instead of repeating the logger.error() command. #189
9. A new class named CustomLogger is created in the pygad.cnn module to create a default logger using the logging module assigned to the logger attribute. This class is extended in all other classes in the module. The constructors of these classes have a new parameter named logger which defaults to None. If no logger is passed, then the default logger in the CustomLogger class is used.
10. Except for the pygad.nn module, the print() function in all other modules are replaced by the logging module to log messages.
11. The callback functions/methods on_fitness(), on_parents(), on_crossover(), and on_mutation() can return values. These returned values override the corresponding properties. The output of on_fitness() overrides the population fitness. The on_parents() function/method must return 2 values representing the parents and their indices. The output of on_crossover() overrides the crossover offspring. The output of on_mutation() overrides the mutation offspring.
12. Fix a bug when adaptive mutation is used while fitness_batch_size>1. #195
13. When allow_duplicate_genes=False and a user-defined gene_space is used, it sometimes happen that there is no room to solve the duplicates between the 2 genes by simply replacing the value of one gene by another gene. This release tries to solve such duplicates by looking for a third gene that will help in solving the duplicates. Check this section for more information.
14. Use probabilities to select parents using the rank parent selection method. #205
15. The 2 parameters random_mutation_min_val and random_mutation_max_val can accept iterables (list/tuple/numpy.ndarray) with length equal to the number of genes. This enables customizing the mutation range for each individual gene. #198
16. The 2 parameters init_range_low and init_range_high can accept iterables (list/tuple/numpy.ndarray) with length equal to the number of genes. This enables customizing the initial range for each individual gene when creating the initial population.
17. The data parameter in the predict() function of the pygad.kerasga module can be assigned a data generator. #115 #207
18. The predict() function of the pygad.kerasga module accepts 3 optional parameters: 1) batch_size=None, verbose=0, and steps=None. Check documentation of the Keras Model.predict() method for more information. #207
19. The documentation is updated to explain how mutation works when gene_space is used with int or float data types. Check this section. #198

Release Date 7 September 2023

1. A new module pygad.utils.nsga2 is created that has the NSGA2 class that includes the functionalities of NSGA-II. The class has these methods: 1) get_non_dominated_set() 2) non_dominated_sorting() 3) crowding_distance() 4) sort_solutions_nsga2(). Check this section for an example.
2. Support of multi-objective optimization using Non-Dominated Sorting Genetic Algorithm II (NSGA-II) using the NSGA2 class in the pygad.utils.nsga2 module. Just return a list, tuple, or numpy.ndarray from the fitness function and the library will consider the problem as multi-objective optimization. All the objectives are expected to be maximization. Check this section for an example.
3. The parent selection methods and adaptive mutation are edited to support multi-objective optimization.
4. Two new NSGA-II parent selection methods are supported in the pygad.utils.parent_selection module: 1) Tournament selection for NSGA-II 2) NSGA-II selection.
5. The plot_fitness() method in the pygad.plot module has a new optional parameter named label to accept the label of the plots. This is only used for multi-objective problems. Otherwise, it is ignored. It defaults to None and accepts a list, tuple, or numpy.ndarray. The labels are used in a legend inside the plot.
6. The default color in the methods of the pygad.plot module is changed to the greenish #64f20c color.
7. A new instance attribute named pareto_fronts added to the pygad.GA instances that holds the pareto fronts when solving a multi-objective problem.
8. The gene_type accepts a list, tuple, or numpy.ndarray for integer data types given that the precision is set to None (e.g. gene_type=[float, [int, None]]).
9. In the cal_pop_fitness() method, the fitness value is re-used if save_best_solutions=True and the solution is found in the best_solutions attribute. These parameters also can help re-using the fitness of a solution instead of calling the fitness function: keep_elitism, keep_parents, and save_solutions.
10. The value 99999999999 is replaced by float('inf') in the 2 methods wheel_cumulative_probs() and stochastic_universal_selection() inside the pygad.utils.parent_selection.ParentSelection class.
11. The plot_result() method in the pygad.visualize.plot.Plot class is removed. Instead, please use the plot_fitness() if you did not upgrade yet.

Release Date 29 January 2024

1. Solve bugs when multi-objective optimization is used. #238
2. When the stop_ciiteria parameter is used with the reach keyword, then multiple numeric values can be passed when solving a multi-objective problem. For example, if a problem has 3 objective functions, then stop_criteria="reach_10_20_30" means the GA stops if the fitness of the 3 objectives are at least 10, 20, and 30, respectively. The number values must match the number of objective functions. If a single value found (e.g. stop_criteria=reach_5) when solving a multi-objective problem, then it is used across all the objectives. #238
3. The delay_after_gen parameter is now deprecated and will be removed in a future release. If it is necessary to have a time delay after each generation, then assign a callback function/method to the on_generation parameter to pause the evolution.
4. Parallel processing now supports calculating the fitness during adaptive mutation. #201
5. The population size can be changed during runtime by changing all the parameters that would affect the size of any thing used by the GA. For more information, check the Change Population Size during Runtime section. #234
6. When a dictionary exists in the gene_space parameter without a step, then mutation occurs by adding a random value to the gene value. The random vaue is generated based on the 2 parameters random_mutation_min_val and random_mutation_max_val. For more information, check the How Mutation Works with the gene_space Parameter? section. #229
7. Add object as a supported data type for int (GA.supported_int_types) and float (GA.supported_float_types). #174
8. Use the raise clause instead of the sys.exit(-1) to terminate the execution. #213
9. Fix a bug when multi-objective optimization is used with batch fitness calculation (e.g. fitness_batch_size set to a non-zero number).
10. Fix a bug in the pygad.py script when finding the index of the best solution. It does not work properly with multi-objective optimization where self.best_solutions_fitness have multiple columns.

            self.best_solution_generation = numpy.where(numpy.array(
                self.best_solutions_fitness) == numpy.max(numpy.array(self.best_solutions_fitness)))[0][0]

Release Date 17 February 2024

1. After the last generation and before the run() method completes, update the 2 instance attributes: 1) last_generation_parents 2) last_generation_parents_indices. This is to keep the list of parents up-to-date with the latest population fitness last_generation_fitness. #275
2. 5 methods with names starting with run_. Their purpose is to keep the main loop inside the run() method clean. Check the Other Methods section for more information.
   1. run_loop_head(): The code before the loop starts.
   2. run_select_parents(): The parent selection-related code.
   3. run_crossover(): The crossover-related code.
   4. run_mutation(): The mutation-related code.
   5. run_update_population(): Update the population instance attribute after completing the processes of crossover and mutation.

Release Date 07 January 2025

1. The delay_after_gen parameter is removed from the pygad.GA class constructor. As a result, it is no longer an attribute of the pygad.GA class instances. To add a delay after each generation, apply it inside the on_generation callback. #283
2. In the single_point_crossover() method of the pygad.utils.crossover.Crossover class, all the random crossover points are returned before the for loop. This is by calling the numpy.random.randint() function only once before the loop to generate all the K points (where K is the offspring size). This is compared to calling the numpy.random.randint() function inside the for loop K times, once for each individual offspring.
3. Bug fix in the examples/example_custom_operators.py script. #285
4. While making prediction using the pygad.torchga.predict() function, no gradients are calculated.
5. The gene_type parameter of the pygad.helper.unique.Unique.unique_int_gene_from_range() method accepts the type of the current gene only instead of the full gene_type list.
6. Created a new method called unique_float_gene_from_range() inside the pygad.helper.unique.Unique class to find a unique floating-point number from a range.
7. Fix a bug in the pygad.helper.unique.Unique.unique_gene_by_space() method to return the numeric value only instead of a NumPy array.
8. Refactoring the pygad/helper/unique.py script to remove duplicate codes and reformatting the docstrings.
9. The plot_pareto_front_curve() method added to the pygad.visualize.plot.Plot class to visualize the Pareto front for multi-objective problems. It only supports 2 objectives. #279
10. Fix a bug converting a nested NumPy array to a nested list. #300
11. The Matplotlib library is only imported when a method inside the pygad/visualize/plot.py script is used. This is more efficient than using import matplotlib.pyplot at the module level as this causes it to be imported when pygad is imported even when it is not needed. #292
12. Fix a bug when minus sign (-) is used inside the stop_criteria parameter (e.g. stop_criteria=["saturate_10", "reach_-0.5"]). #296
13. Make sure self.best_solutions is a list of lists inside the cal_pop_fitness method. #293
14. Fix a bug where the cal_pop_fitness() method was using the previous_generation_fitness attribute to return the parents fitness. This instance attribute was not using the fitness of the latest population, instead the fitness of the population before the last one. The issue is solved by updating the previous_generation_fitness attribute to the latest population fitness before the GA completes. #291

Release Date 08 July 2025

1. Fix a bug when minus sign (-) is used inside the stop_criteria parameter for multi-objective problems. #314 #323
2. Fix a bug when the stop_criteria parameter is passed as an iterable (e.g. list) for multi-objective problems (e.g. ['reach_50_60', 'reach_20, 40']). #314
3. Call the get_matplotlib() function from the plot_genes() method inside the pygad.visualize.plot.Plot class to import the matplotlib library. #315
4. Create a new helper method called select_unique_value() inside the pygad/helper/unique.py script to select a unique gene from an array of values.
5. Create a new helper method called get_random_mutation_range() inside the pygad/utils/mutation.py script that returns the random mutation range (min and max) for a single gene by its index.
6. Create a new helper method called change_random_mutation_value_dtype inside the pygad/utils/mutation.py script that changes the data type of the value used to apply random mutation.
7. Create a new helper method called round_random_mutation_value() inside the pygad/utils/mutation.py script that rounds the value used to apply random mutation.
8. Create the pygad/helper/misc.py script with a class called Helper that has the following helper methods:
   1. change_population_dtype_and_round(): For each gene in the population, round the gene value and change the data type.
   2. change_gene_dtype_and_round(): Round the change the data type of a single gene.
   3. mutation_change_gene_dtype_and_round(): Decides whether mutation is done by replacement or not. Then it rounds and change the data type of the new gene value.
   4. validate_gene_constraint_callable_output(): Validates the output of the user-defined callable/function that checks whether the gene constraint defined in the gene_constraint parameter is satisfied or not.
   5. get_gene_dtype(): Returns the gene data type from the gene_type instance attribute.
   6. get_random_mutation_range(): Returns the random mutation range using the random_mutation_min_val and random_mutation_min_val instance attributes.
   7. get_initial_population_range(): Returns the initial population values range using the init_range_low and init_range_high instance attributes.
   8. generate_gene_value_from_space(): Generates/selects a value for a gene using the gene_space instance attribute.
   9. generate_gene_value_randomly(): Generates a random value for the gene. Only used if gene_space is None.
   10. generate_gene_value(): Generates a value for the gene. It checks whether gene_space is None and calls either generate_gene_value_randomly() or generate_gene_value_from_space().
   11. filter_gene_values_by_constraint(): Receives a list of values for a gene. Then it filters such values using the gene constraint.
   12. get_valid_gene_constraint_values(): Selects one valid gene value that satisfy the gene constraint. It simply calls generate_gene_value() to generate some gene values then it filters such values using filter_gene_values_by_constraint().
9. Create a new helper method called mutation_process_random_value() inside the pygad/utils/mutation.py script that generates constrained random values for mutation. It calls either generate_gene_value() or get_valid_gene_constraint_values() based on whether the gene_constraint parameter is used or not.
10. A new parameter called gene_constraint is added. It accepts a list of callables (i.e. functions) acting as constraints for the gene values. Before selecting a value for a gene, the callable is called to ensure the candidate value is valid. Check the Gene Constraint section for more information. #119
11. A new parameter called sample_size is added. To select a gene value that respects a constraint, this variable defines the size of the sample from which a value is selected randomly. Useful if either allow_duplicate_genes or gene_constraint is used. An instance attribute of the same name is created in the instances of the pygad.GA class. Check the sample_size Parameter section for more information.
12. Use the sample_size parameter instead of num_trials in the methods solve_duplicate_genes_randomly() and unique_float_gene_from_range() inside the pygad/helper/unique.py script. It is the maximum number of values to generate as the search space when looking for a unique float value out of a range.
13. Fixed a bug in population initialization when allow_duplicate_genes=False. Previously, gene values were checked for duplicates before rounding, which could allow near-duplicates like 7.61 and 7.62 to pass. After rounding (e.g., both becoming 7.6), this resulted in unintended duplicates. The fix ensures gene values are now rounded before duplicate checks, preventing such cases.
14. More tests are created.
15. More examples are created.
16. Edited the sort_solutions_nsga2() method in the pygad/utils/nsga2.py script to accept an optional parameter called find_best_solution when calling this method just to find the best solution.
17. Fixed a bug while applying the non-dominated sorting in the get_non_dominated_set() method inside the pygad/utils/nsga2.py script. It was swapping the non-dominated and dominated sets. In other words, it used the non-dominated set as if it is the dominated set and vice versa. All the calls to this method were edited accordingly. #320.
18. Fix a bug retrieving in the best_solution() method when retrieving the best solution for multi-objective problems. #331

Release Date April 8, 2026

1. Support passing a class to the fitness, crossover, and mutation. #342
2. A new class called Validation is created in the new pygad/utils/validation.py script. It has a method called validate_parameters() to validate all the parameters passed while instantiating the pygad.GA class.
3. Refactoring the pygad.py script by moving a lot of functions and methods to other classes in other scripts.
4. The summary() method was moved to Helper class in the pygad/helper/misc.py script.
5. The validation code in the __init__() method of the pygad.GA class is moved to the new validate_parameters() method in the new Validation class in the new pygad/utils/validation.py script. Moreover, the validate_multi_stop_criteria() method is also moved to the same class.
6. The GA main workflow is moved into the new GAEngine class in the new pygad/utils/engine.py script. Specifically, these methods are moved from the pygad.GA class to the new GAEngine class: 1. run() 1. run_loop_head() 2. run_select_parents() 3. run_crossover() 4. run_mutation() 5. run_update_population() 2. initialize_population() 3. cal_pop_fitness() 4. best_solution() 5. round_genes()
7. The pygad.GA class now extends the two new classes utils.validation.Validation and utils.engine.GAEngine.
8. The version of the pygad.utils submodule is upgraded from 1.3.0 to 1.4.0.
9. The version of the pygad.helper submodule is upgraded from 1.2.0 to 1.3.0.
10. The version of the pygad.visualize submodule is upgraded from 1.1.0 to 1.1.1.
11. The version of the pygad.nn submodule is upgraded from 1.2.1 to 1.2.2.
12. The version of the pygad.cnn submodule is upgraded from 1.1.0 to 1.1.1.
13. The version of the pygad.torchga submodule is upgraded from 1.4.0 to 1.4.1.
14. The version of the pygad.kerasga submodule is upgraded from 1.3.0 to 1.3.1.
15. Update the elitism after the evolution ends to fix issue where the best solution returned by the best_solution() method is not correct. #337
16. Fix a bug in calling the numpy.reshape() function. The parameter newshape is removed since it is no longer supported started from NumPy 2.4.0. https://numpy.org/doc/stable/release/2.4.0-notes.html#removed-newshape-parameter-from-numpy-reshape
17. A minor change in the documentation is made to replace the newshape parameter when calling numpy.reshape().
18. Fix a bug in the visualize/plot.py script that causes a warning to be given when the plot leged is used with single-objective problems.
19. A new method called initialize_parents_array() is added to the Helper class in the pygad/helper/misc.py script. It is usually called from the methods in the ParentSelection class in the pygad/utils/parent_selection.py script to initialize the parents array.
20. Add more tests about: 1. Operators (crossover, mutation, and parent selection). 2. The best_solution() method. 3. Parallel processing. 4. The GANN module. 5. The plots created by the visualize.
21. Instead of using repeated code for converting the data type and rounding the genes during crossover and mutation, the change_gene_dtype_and_round() method is called from the pygad.helper.misc.Helper class.
22. Fix some documentation issues. #336
23. Update the documentation to reflect the recent additions and changes to the library structure.

Release Date June 5, 2026

Watch the release video on YouTube.

<iframe width="560" height="315" src="https://www.youtube.com/embed/EXMy37crL7c" title="PyGAD 3.7.0 Release" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe>

1. Validation logic is applied to validate the num_generations parameter.
2. The num_generations parameter must be assigned a positive integer. Previously, any number (positive/negative, int/float) was accepted.
3. A new script called activation.py is added into the pygad.helper module to include the activation function used by the cnn and nn modules.
4. In the pygad.parent_selection.ParentSelection class, the stochastic_universal_selection() method now calls the wheel_cumulative_probs() method instead of repeating the code of calculating the probabilities used for parent selection.
5. The wheel_cumulative_probs() method in the pygad.parent_selection.ParentSelection class is refactored to reduce its computational time.
6. Use numpy.where() to decide which the source parent of each gene within the uniform_crossover() method in the utils/crossover.py script. The same was already applied to the scattered_crossover() method.
7. Add tests for the following modules:
   1. nn
   2. cnn
   3. gacnn
   4. kerasga
   5. torchga
8. Fix a bug in the visualize/plot.py script where the labels parameter of boxplot() has been renamed tick_labels in Matplotlib.
9. Fix a bug where the best_solutions_fitness list (instance attribute to pygad.GA) has the fitness of the last generation duplicated when an early stop happens inside the on_generation() callback. This made its size incompatible with the best_solutions list.
10. The documentation is refactored to solve many language issues and the Furo theme is applied. For easy navigation, the index is reformatted to only show the main sections. At each page, its index is shown at the right side. A new theme toggle button to change theme between light and dark.
11. Support of multi-objective optimization using the Non-Dominated Sorting Genetic Algorithm III (NSGA-III). NSGA-III replaces the crowding distance of NSGA-II with niching against a structured grid of reference points, so it scales better to problems with 4 or more objectives. The new NSGA3 class lives in the new pygad/utils/nsga3.py script and is mixed into the pygad.GA class the same way NSGA2 is.
12. Two new parent selection methods are added to support NSGA-III: 1) nsga3_selection() for plain NSGA-III selection, and 2) tournament_selection_nsga3() for the tournament variant. Use them by setting parent_selection_type to 'nsga3' or 'tournament_nsga3'.
13. A new parameter nsga3_num_divisions is added to the pygad.GA constructor. It is required when parent_selection_type is 'nsga3' or 'tournament_nsga3' and sets the number of divisions per objective axis used to build the structured reference points (the p parameter from Deb & Jain 2014). The total number of reference points is C(M + p - 1, p) where M is the number of objectives.
14. When sol_per_pop is smaller than the number of NSGA-III reference points, PyGAD raises a warning and grows the population to match before the generational loop starts.
15. A new crossover operator: Simulated Binary Crossover (SBX). Use it by setting crossover_type='sbx'. The shape of the spread is controlled by the new sbx_crossover_eta parameter (default 30).
16. A new mutation operator: polynomial mutation. Use it by setting mutation_type='polynomial'. The size of the change is controlled by the new polynomial_mutation_eta parameter (default 20).
17. Two new stop criteria: time_<seconds> stops the run when the time inside run() is at least the given number of seconds; evaluations_<N> stops the run when the number of fitness function calls reaches the given count. New instance attribute num_fitness_evaluations counts the calls.
18. A new submodule pygad.utils.quality_indicators with four functions to measure the quality of a Pareto front: hypervolume, inverted_generational_distance, generational_distance, and spacing.
19. A new submodule pygad.benchmarks with built-in benchmark problems. pygad.benchmarks.classic has Sphere, Rastrigin, Rosenbrock, Griewank, Schwefel, Ackley, and Himmelblau. pygad.benchmarks.zdt has the ZDT family (ZDT1, ZDT2, ZDT3, ZDT4, ZDT6). pygad.benchmarks.dtlz has DTLZ1, DTLZ2, DTLZ3, and DTLZ4. pygad.benchmarks.knapsack has the 0/1 Knapsack problem. Each class is callable with the PyGAD fitness signature and returns negated values (for the minimization-style problems) so PyGAD can maximize toward the original minimum.
20. Update the documentation to reflect the recent additions and changes to the library structure.
21. A new benchmark pygad.benchmarks.tsp with a TSP class for the Travelling Salesman Problem. The class accepts either 2D coordinates or a precomputed distance_matrix, exposes gene_space, gene_type, and allow_duplicate_genes for the permutation encoding, and returns the negative tour length as the fitness.
22. Two new example folders under /examples: examples/benchmarks/ has one runnable example per benchmark (classic, ZDT, DTLZ, knapsack, and TSP), and examples/quality_indicators/ has one runnable example per quality indicator (hypervolume, IGD, GD, and spacing).
23. plot_pareto_front_curve() now also supports 3 objectives (3D scatter). M >= 4 still raises and points to the new high-dimensional plots.
24. Seven new plot methods on pygad.GA. The first three work on the final population (no extra flag needed): plot_pareto_front_pcp() (parallel coordinates, any M >= 2), plot_pareto_front_scatter_matrix() (M-by-M pairwise scatter, best for M >= 4), and plot_pareto_front_heatmap() (solutions-by-objectives heatmap). The other four require save_solutions=True: plot_fitness_band() (per-generation min / mean / max with a shaded band), plot_non_dominated_hypervolume() (hypervolume of the non-dominated set per generation), plot_population_diversity() (mean pairwise distance per generation), and plot_pareto_front_evolution() (non-dominated set overlaid every k generations).
25. Fix a latent divide-by-zero in NSGA3.nsga3_normalize_fitness(). The safeguard for near-zero denominators used to collapse to 0 for tiny negative values (the realistic case under PyGAD-max), which silently produced wrong normalized values. The safeguard now keeps the negative sign.
26. Refactor the NSGA classes to keep each script focused. A new module pygad/utils/nsga.py hosts the NSGA mixin with non_dominated_sorting() and get_non_dominated_set(), which are shared between NSGA-II and NSGA-III. nsga2.py now only carries NSGA-II specific code (crowding_distance, sort_solutions_nsga2). nsga3.py now only carries the NSGA-III algorithm primitives. The nsga3_selection() and tournament_selection_nsga3() methods have moved to pygad/utils/parent_selection.py next to their NSGA-II counterparts. The engine-time helpers _bootstrap_nsga3_reference_points(), _nsga3_grow_population(), _nsga3_generate_extra_random_solutions(), and _nsga3_generate_single_random_gene() now live in pygad/utils/engine.py.
27. Rename NSGA-III novel names to start with nsga3_ so the algorithm-specific surface is easy to spot. Algorithm primitives become nsga3_generate_reference_points, nsga3_compute_ideal_point, nsga3_find_extreme_points, nsga3_compute_intercepts, nsga3_normalize_fitness, nsga3_associate_to_reference_points, and nsga3_niching_select. Module-level helpers gain the same prefix (_nsga3_pick_target_reference_point, _nsga3_pick_candidate_at_reference, _nsga3_enumerate_compositions, _nsga3_validate_multi_objective_fitness, _nsga3_accumulate_fronts). The constants are renamed NSGA3_ASF_EPSILON and NSGA3_INTERCEPT_NEAR_ZERO. Names that already had NSGA-II parallels (tournament_selection_nsga3, pareto_fronts, non_dominated_sorting) keep their original spelling.
28. Spell every name and docstring in American English (normalize, maximize, behavior, color, optimization, ...) so the library stays consistent.
29. Expand abbreviated names introduced by the NSGA-III refactor: fl_indices to critical_front_indices, fl_assoc to critical_front_associations, fl_dist to critical_front_distances, st_indices to selection_pool_indices, st_fitness to selection_pool_fitness, accepted_assoc to accepted_associations, K to num_to_select (in nsga3_niching_select).
30. The NSGA-III population auto-growth path now respects every initial-population rule: init_range_low/init_range_high, gene_space, gene_type (single dtype or nested per-gene [type, precision]), gene_constraint, and allow_duplicate_genes=False. Previously, only the gene-space / init-range sampling step was applied; gene constraints and duplicate resolution were skipped, which could leave the grown rows in an invalid state.
31. A new Report mixin in pygad/utils/report.py adds ga_instance.generate_report(filename, ...) to build a PDF report of the run. The report bundles a configuration table, a run-summary table, the best solution, and every applicable plot (auto-selected based on the run's properties: SOO vs MOO, number of objectives, save_solutions, save_best_solutions). The report uses reportlab and matplotlib, both available through the new optional dependency extra pip install pygad[report].
32. A new example examples/example_generate_report.py shows how to build a PDF report after running a multi-objective GA.
33. The pygad.md, releases.md, visualize.md, and utils.md documentation pages were updated to reflect the new module layout, the renamed methods, the new generate_report() entry point, and the new NSGA-III instance attributes (nsga3_num_divisions, nsga3_reference_points). The "Other Instance Attributes & Methods" section in pygad.md is now grouped by area (Lifecycle, Population, Fitness, Parent Selection, NSGA-II, NSGA-III, Crossover, Mutation, Elitism, Gene Constraints, Saving) so each method or attribute appears under its topic.
34. Fix issue #351 by updating the documentation to clarify what the solution has.
35. Version changed in the following modules:
    1. A new submodule pygad.benchmarks is added with the version 1.0.0.
    2. The version of the pygad.utils submodule is upgraded from 1.4.0 to 1.5.0.
    3. The version of the pygad.helper submodule is upgraded from 1.3.0 to 1.4.0.
    4. The version of the pygad.visualize submodule is upgraded from 1.1.1 to 1.2.0.
    5. The version of the pygad.nn submodule is upgraded from 1.2.2 to 1.2.3.
    6. The version of the pygad.cnn submodule is upgraded from 1.1.1 to 1.1.2.
    7. The version of the pygad.kerasga submodule is upgraded from 1.3.1 to 1.3.2.
    8. The version of the pygad.torchga submodule is upgraded from 1.4.1 to 1.4.2.
    9. The version of the pygad.gann submodule is upgraded from 1.0.0 to 1.0.1.
    10. The version of the pygad.gacnn submodule is upgraded from 1.0.0 to 1.0.1.
36. The PDF report built by generate_report() now shows the PyGAD logo on its title page. The logo image ships with the package, so no network access is needed. If the image file is missing, the report is built without it.
37. Two private helper functions are added to the pygad/utils/report.py script for the logo. _pdf_report_read_logo_bytes() reads the bundled logo file and returns its bytes, or None if the file is missing. _pdf_report_build_logo_image() builds the image that is placed on the title page, or returns None so the report still builds without the logo.
38. The private helper functions in the pygad/utils/report.py script are renamed to start with the _pdf_report_ prefix so their purpose is clear from the name. For example, _build_title_section() becomes _pdf_report_build_title_section() and _render_plot_to_png() becomes _pdf_report_render_plot_to_png().