def validate_parameters(self,

num_generations,

num_parents_mating,

fitness_func,

fitness_batch_size,

initial_population,

sol_per_pop,

num_genes,

init_range_low,

init_range_high,

gene_type,

parent_selection_type,

keep_parents,

keep_elitism,

K_tournament,

crossover_type,

crossover_probability,

mutation_type,

mutation_probability,

mutation_by_replacement,

mutation_percent_genes,

mutation_num_genes,

random_mutation_min_val,

random_mutation_max_val,

gene_space,

gene_constraint,

sample_size,

allow_duplicate_genes,

on_start,

on_fitness,

on_parents,

on_crossover,

on_mutation,

on_generation,

on_stop,

save_best_solutions,

save_solutions,

suppress_warnings,

stop_criteria,

parallel_processing,

random_seed,

logger):

# If no logger is passed, then create a logger that logs only the messages to the console.

if logger is None:

# Create a logger named with the module name.

logger = logging.getLogger(__name__)

# Set the logger log level to 'DEBUG' to log all kinds of messages.

logger.setLevel(logging.DEBUG)

# Clear any attached handlers to the logger from the previous runs.

logger.handlers.clear()

# Create the handlers.

stream_handler = logging.StreamHandler()

# Set the handler log level to 'DEBUG' to log all kinds of messages received from the logger.

stream_handler.setLevel(logging.DEBUG)

# Create the formatter that just includes the log message.

formatter = logging.Formatter('%(message)s')

# Add the formatter to the handler.

stream_handler.setFormatter(formatter)

# Add the handler to the logger.

logger.addHandler(stream_handler)

else:

# Validate that the passed logger is of type 'logging.Logger'.

if isinstance(logger, logging.Logger):

pass

else:

self.valid_parameters = False

raise TypeError(f"The expected type of the 'logger' parameter is 'logging.Logger' but {type(logger)} found.")

# Create the 'self.logger' attribute to hold the logger.

self.logger = logger

self.random_seed = random_seed

if random_seed is None:

pass

else:

numpy.random.seed(self.random_seed)

random.seed(self.random_seed)

# If suppress_warnings is bool and its value is False, then print warning messages.

if type(suppress_warnings) is bool:

self.suppress_warnings = suppress_warnings

else:

self.valid_parameters = False

raise TypeError(f"The expected type of the 'suppress_warnings' parameter is bool but {type(suppress_warnings)} found.")

# Validating mutation_by_replacement

if not (type(mutation_by_replacement) is bool):

self.valid_parameters = False

raise TypeError(f"The expected type of the 'mutation_by_replacement' parameter is bool but {type(mutation_by_replacement)} found.")

self.mutation_by_replacement = mutation_by_replacement

# Validate the sample_size parameter.

if type(sample_size) in self.supported_int_types:

if sample_size > 0:

pass

else:

self.valid_parameters = False

raise ValueError(f"The value of the sample_size parameter must be > 0 but the value ({sample_size}) found.")

else:

self.valid_parameters = False

raise TypeError(f"The type of the sample_size parameter must be integer but the value ({sample_size}) of type {type(sample_size)} found.")

self.sample_size = sample_size

# Validate allow_duplicate_genes

if not (type(allow_duplicate_genes) is bool):

self.valid_parameters = False

raise TypeError(f"The expected type of the 'allow_duplicate_genes' parameter is bool but {type(allow_duplicate_genes)} found.")

self.allow_duplicate_genes = allow_duplicate_genes

# Validate gene_space

self.gene_space_nested = False

if type(gene_space) is type(None):

pass

elif type(gene_space) is range:

if len(gene_space) == 0:

self.valid_parameters = False

raise ValueError("'gene_space' cannot be empty (i.e. its length must be >= 0).")

elif type(gene_space) in [list, numpy.ndarray]:

if len(gene_space) == 0:

self.valid_parameters = False

raise ValueError("'gene_space' cannot be empty (i.e. its length must be >= 0).")

else:

for index, el in enumerate(gene_space):

if type(el) in [numpy.ndarray, list, tuple, range]:

if len(el) == 0:

self.valid_parameters = False

raise ValueError(f"The element indexed {index} of 'gene_space' with type {type(el)} cannot be empty (i.e. its length must be >= 0).")

else:

for val in el:

if not (type(val) in [type(None)] + self.supported_int_float_types):

raise TypeError(f"All values in the sublists inside the 'gene_space' attribute must be numeric of type int/float/None but ({val}) of type {type(val)} found.")

self.gene_space_nested = True

elif type(el) == type(None):

pass

elif type(el) is dict:

if len(el.items()) == 2:

if ('low' in el.keys()) and ('high' in el.keys()):

pass

else:

self.valid_parameters = False

raise ValueError(f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}")

elif len(el.items()) == 3:

if ('low' in el.keys()) and ('high' in el.keys()) and ('step' in el.keys()):

pass

else:

self.valid_parameters = False

raise ValueError(f"When an element in the 'gene_space' parameter is of type dict, then it can have the keys 'low', 'high', and 'step' (optional) but the following keys found: {el.keys()}")

else:

self.valid_parameters = False

raise ValueError(f"When an element in the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(el.items())}) items found.")

self.gene_space_nested = True

elif not (type(el) in self.supported_int_float_types):

self.valid_parameters = False

raise TypeError(f"Unexpected type {type(el)} for the element indexed {index} of 'gene_space'. The accepted types are list/tuple/range/numpy.ndarray of numbers, a single number (int/float), or None.")

elif type(gene_space) is dict:

if len(gene_space.items()) == 2:

if ('low' in gene_space.keys()) and ('high' in gene_space.keys()):

pass

else:

self.valid_parameters = False

raise ValueError(f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}")

elif len(gene_space.items()) == 3:

if ('low' in gene_space.keys()) and ('high' in gene_space.keys()) and ('step' in gene_space.keys()):

pass

else:

self.valid_parameters = False

raise ValueError(f"When the 'gene_space' parameter is of type dict, then it can have only the keys 'low', 'high', and 'step' (optional) but the following keys found: {gene_space.keys()}")

else:

self.valid_parameters = False

raise ValueError(f"When the 'gene_space' parameter is of type dict, then it must have only 2 items but ({len(gene_space.items())}) items found.")

else:

self.valid_parameters = False

raise TypeError(f"The expected type of 'gene_space' is list, range, or numpy.ndarray but {type(gene_space)} found.")

self.gene_space = gene_space

# Validate init_range_low and init_range_high

if type(init_range_low) in self.supported_int_float_types:

if type(init_range_high) in self.supported_int_float_types:

if init_range_low == init_range_high:

if not self.suppress_warnings:

warnings.warn("The values of the 2 parameters 'init_range_low' and 'init_range_high' are equal and this might return the same value for some genes in the initial population.")

else:

self.valid_parameters = False

raise TypeError(f"Type mismatch between the 2 parameters 'init_range_low' {type(init_range_low)} and 'init_range_high' {type(init_range_high)}.")

elif type(init_range_low) in [list, tuple, numpy.ndarray]:

# Get the number of genes before validating the num_genes parameter.

if num_genes is None:

if initial_population is None:

self.valid_parameters = False

raise TypeError("When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.")

elif not len(init_range_low) == len(initial_population[0]):

self.valid_parameters = False

raise ValueError(f"The length of the 'init_range_low' parameter is {len(init_range_low)} which is different from the number of genes {len(initial_population[0])}.")

elif not len(init_range_low) == num_genes:

self.valid_parameters = False

raise ValueError(f"The length of the 'init_range_low' parameter is {len(init_range_low)} which is different from the number of genes {num_genes}.")

if type(init_range_high) in [list, tuple, numpy.ndarray]:

if len(init_range_low) == len(init_range_high):

pass

else:

self.valid_parameters = False

raise ValueError(f"Size mismatch between the 2 parameters 'init_range_low' {len(init_range_low)} and 'init_range_high' {len(init_range_high)}.")

# Validate the values in init_range_low

for val in init_range_low:

if type(val) in self.supported_int_float_types:

pass

else:

self.valid_parameters = False

raise TypeError(f"When an iterable (list/tuple/numpy.ndarray) is assigned to the 'init_range_low' parameter, its elements must be numeric but the value {val} of type {type(val)} found.")

# Validate the values in init_range_high

for val in init_range_high:

if type(val) in self.supported_int_float_types:

pass

else:

self.valid_parameters = False

raise TypeError(f"When an iterable (list/tuple/numpy.ndarray) is assigned to the 'init_range_high' parameter, its elements must be numeric but the value {val} of type {type(val)} found.")

else:

self.valid_parameters = False

raise TypeError(f"Type mismatch between the 2 parameters 'init_range_low' {type(init_range_low)} and 'init_range_high' {type(init_range_high)}. Both of them can be either numeric or iterable (list/tuple/numpy.ndarray).")

else:

self.valid_parameters = False

raise TypeError(f"The expected type of the 'init_range_low' parameter is numeric or list/tuple/numpy.ndarray but {type(init_range_low)} found.")

self.init_range_low = init_range_low

self.init_range_high = init_range_high

# Validate gene_type

if gene_type in self.supported_int_float_types:

self.gene_type = [gene_type, None]

self.gene_type_single = True

# A single data type of float with precision.

elif len(gene_type) == 2 and gene_type[0] in self.supported_float_types and (type(gene_type[1]) in self.supported_int_types or gene_type[1] is None):

self.gene_type = gene_type

self.gene_type_single = True

# A single data type of integer with precision None ([int, None]).

elif len(gene_type) == 2 and gene_type[0] in self.supported_int_types and gene_type[1] is None:

self.gene_type = gene_type

self.gene_type_single = True

# Raise an exception for a single data type of int with integer precision.

elif len(gene_type) == 2 and gene_type[0] in self.supported_int_types and (type(gene_type[1]) in self.supported_int_types or gene_type[1] is None):

self.gene_type_single = False

raise ValueError(f"Integers cannot have precision. Please use the integer data type directly instead of {gene_type}.")

elif type(gene_type) in [list, tuple, numpy.ndarray]:

# Get the number of genes before validating the num_genes parameter.

if num_genes is None:

if initial_population is None:

self.valid_parameters = False

raise TypeError("When the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.")

elif not len(gene_type) == len(initial_population[0]):

self.valid_parameters = False

raise ValueError(f"When the parameter 'gene_type' is nested, then it can be either [float, int<precision>] or with length equal to the number of genes parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != number of genes ({len(initial_population[0])}) found.")

elif not len(gene_type) == num_genes:

self.valid_parameters = False

raise ValueError(f"When the parameter 'gene_type' is nested, then it can be either [float, int<precision>] or with length equal to the value passed to the 'num_genes' parameter. Instead, value {gene_type} with len(gene_type) ({len(gene_type)}) != len(num_genes) ({num_genes}) found.")

for gene_type_idx, gene_type_val in enumerate(gene_type):

if gene_type_val in self.supported_int_float_types:

# If the gene type is float and no precision is passed or an integer, set its precision to None.

gene_type[gene_type_idx] = [gene_type_val, None]

elif type(gene_type_val) in [list, tuple, numpy.ndarray]:

# A float type is expected in a list/tuple/numpy.ndarray of length 2.

if len(gene_type_val) == 2:

if gene_type_val[0] in self.supported_float_types:

if type(gene_type_val[1]) in self.supported_int_types:

pass

else:

self.valid_parameters = False

raise TypeError(f"In the 'gene_type' parameter, the precision for float gene data types must be an integer but the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_val[1]} with type {gene_type_val[0]}.")

elif gene_type_val[0] in self.supported_int_types:

if gene_type_val[1] is None:

pass

else:

self.valid_parameters = False

raise TypeError(f"In the 'gene_type' parameter, either do not set a precision for integer data types or set it to None. But the element {gene_type_val} at index {gene_type_idx} has a precision of {gene_type_val[1]} with type {gene_type_val[0]}.")

else:

self.valid_parameters = False

raise TypeError(

f"In the 'gene_type' parameter, a precision is expected only for float gene data types but the element {gene_type_val} found at index {gene_type_idx}.\nNote that the data type must be at index 0 of the item followed by precision at index 1.")

else:

self.valid_parameters = False

raise ValueError(f"In the 'gene_type' parameter, a precision is specified in a list/tuple/numpy.ndarray of length 2 but value ({gene_type_val}) of type {type(gene_type_val)} with length {len(gene_type_val)} found at index {gene_type_idx}.")

else:

self.valid_parameters = False

raise ValueError(f"When a list/tuple/numpy.ndarray is assigned to the 'gene_type' parameter, then its elements must be of integer, floating-point, list, tuple, or numpy.ndarray data types but the value ({gene_type_val}) of type {type(gene_type_val)} found at index {gene_type_idx}.")

self.gene_type = gene_type

self.gene_type_single = False

else:

self.valid_parameters = False

raise ValueError(f"The value passed to the 'gene_type' parameter must be either a single integer, floating-point, list, tuple, or numpy.ndarray but ({gene_type}) of type {type(gene_type)} found.")

# Call the unpack_gene_space() method in the pygad.helper.unique.Unique class.

self.gene_space_unpacked = self.unpack_gene_space(range_min=self.init_range_low,

range_max=self.init_range_high)

# Build the initial population

if initial_population is None:

if (sol_per_pop is None) or (num_genes is None):

self.valid_parameters = False

raise TypeError("Error creating the initial population:\n\nWhen the parameter 'initial_population' is None, then the 2 parameters 'sol_per_pop' and 'num_genes' cannot be None too.\nThere are 2 options to prepare the initial population:\n1) Assigning the initial population to the 'initial_population' parameter. In this case, the values of the 2 parameters sol_per_pop and num_genes will be deduced.\n2) Assign integer values to the 'sol_per_pop' and 'num_genes' parameters so that PyGAD can create the initial population automatically.")

elif (type(sol_per_pop) is int) and (type(num_genes) is int):

# Validating the number of solutions in the population (sol_per_pop)

if sol_per_pop <= 0:

self.valid_parameters = False

raise ValueError(f"The number of solutions in the population (sol_per_pop) must be > 0 but ({sol_per_pop}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n")

# Validating the number of gene.

if (num_genes <= 0):

self.valid_parameters = False

raise ValueError(f"The number of genes cannot be <= 0 but ({num_genes}) found.\n")

# When initial_population=None and the 2 parameters sol_per_pop and num_genes have valid integer values, then the initial population is created.

# Inside the initialize_population() method, the initial_population attribute is assigned to keep the initial population accessible.

self.num_genes = num_genes # Number of genes in the solution.

# In case the 'gene_space' parameter is nested, then make sure the number of its elements equals to the number of genes.

if self.gene_space_nested:

if len(gene_space) != self.num_genes:

self.valid_parameters = False

raise ValueError(f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})")

# Number of solutions in the population.

self.sol_per_pop = sol_per_pop

self.initialize_population(allow_duplicate_genes=allow_duplicate_genes,

gene_type=self.gene_type,

gene_constraint=gene_constraint)

else:

self.valid_parameters = False

raise TypeError(f"The expected type of both the sol_per_pop and num_genes parameters is int but {type(sol_per_pop)} and {type(num_genes)} found.")

elif not type(initial_population) in [list, tuple, numpy.ndarray]:

self.valid_parameters = False

raise TypeError(f"The value assigned to the 'initial_population' parameter is expected to be of type list, tuple, or ndarray but {type(initial_population)} found.")

elif numpy.array(initial_population).ndim != 2:

self.valid_parameters = False

raise ValueError(f"A 2D list is expected to the initial_population parameter but a ({numpy.array(initial_population).ndim}-D) list found.")

else:

# Validate the type of each value in the 'initial_population' parameter.

for row_idx in range(len(initial_population)):

for col_idx in range(len(initial_population[0])):

if type(initial_population[row_idx][col_idx]) in self.supported_int_float_types:

pass

else:

self.valid_parameters = False

raise TypeError(f"The values in the initial population can be integers or floats but the value ({initial_population[row_idx][col_idx]}) of type {type(initial_population[row_idx][col_idx])} found.")

# Change the data type and round all genes within the initial population.

self.initial_population = self.change_population_dtype_and_round(initial_population)

# Check if duplicates are allowed. If not, then solve any existing duplicates in the passed initial population.

if self.allow_duplicate_genes == False:

for initial_solution_idx, initial_solution in enumerate(self.initial_population):

if self.gene_space is None:

self.initial_population[initial_solution_idx], _, _ = self.solve_duplicate_genes_randomly(solution=initial_solution,

min_val=self.init_range_low,

max_val=self.init_range_high,

mutation_by_replacement=True,

gene_type=self.gene_type,

sample_size=self.sample_size)

else:

self.initial_population[initial_solution_idx], _, _ = self.solve_duplicate_genes_by_space(solution=initial_solution,

gene_type=self.gene_type,

sample_size=self.sample_size,

mutation_by_replacement=True,

build_initial_pop=True)

# A NumPy array holding the initial population.

self.population = self.initial_population.copy()

# Number of genes in the solution.

self.num_genes = self.initial_population.shape[1]

# Number of solutions in the population.

self.sol_per_pop = self.initial_population.shape[0]

# The population size.

self.pop_size = (self.sol_per_pop, self.num_genes)

# Change the data type and round all genes within the initial population.

self.initial_population = self.change_population_dtype_and_round(self.initial_population)

self.population = self.initial_population.copy()

# In case the 'gene_space' parameter is nested, then make sure the number of its elements equals to the number of genes.

if self.gene_space_nested:

if len(gene_space) != self.num_genes:

self.valid_parameters = False

raise ValueError(f"When the parameter 'gene_space' is nested, then its length must be equal to the value passed to the 'num_genes' parameter. Instead, length of gene_space ({len(gene_space)}) != num_genes ({self.num_genes})")

# Validate random_mutation_min_val and random_mutation_max_val

if type(random_mutation_min_val) in self.supported_int_float_types:

if type(random_mutation_max_val) in self.supported_int_float_types:

if random_mutation_min_val == random_mutation_max_val:

if not self.suppress_warnings:

warnings.warn("The values of the 2 parameters 'random_mutation_min_val' and 'random_mutation_max_val' are equal and this might cause a fixed mutation to some genes.")

else:

self.valid_parameters = False

raise TypeError(f"Type mismatch between the 2 parameters 'random_mutation_min_val' {type(random_mutation_min_val)} and 'random_mutation_max_val' {type(random_mutation_max_val)}.")

elif type(random_mutation_min_val) in [list, tuple, numpy.ndarray]:

if len(random_mutation_min_val) == self.num_genes:

pass

else:

self.valid_parameters = False

raise ValueError(f"The length of the 'random_mutation_min_val' parameter is {len(random_mutation_min_val)} which is different from the number of genes {self.num_genes}.")

if type(random_mutation_max_val) in [list, tuple, numpy.ndarray]:

if len(random_mutation_min_val) == len(random_mutation_max_val):

pass

else:

self.valid_parameters = False

raise ValueError(f"Size mismatch between the 2 parameters 'random_mutation_min_val' {len(random_mutation_min_val)} and 'random_mutation_max_val' {len(random_mutation_max_val)}.")

# Validate the values in random_mutation_min_val

for val in random_mutation_min_val:

if type(val) in self.supported_int_float_types:

pass

else:

self.valid_parameters = False

raise TypeError(f"When an iterable (list/tuple/numpy.ndarray) is assigned to the 'random_mutation_min_val' parameter, its elements must be numeric but the value {val} of type {type(val)} found.")

# Validate the values in random_mutation_max_val

for val in random_mutation_max_val:

if type(val) in self.supported_int_float_types:

pass

else:

self.valid_parameters = False

raise TypeError(f"When an iterable (list/tuple/numpy.ndarray) is assigned to the 'random_mutation_max_val' parameter, its elements must be numeric but the value {val} of type {type(val)} found.")

else:

self.valid_parameters = False

raise TypeError(f"Type mismatch between the 2 parameters 'random_mutation_min_val' {type(random_mutation_min_val)} and 'random_mutation_max_val' {type(random_mutation_max_val)}.")

else:

self.valid_parameters = False

raise TypeError(f"The expected type of the 'random_mutation_min_val' parameter is numeric or list/tuple/numpy.ndarray but {type(random_mutation_min_val)} found.")

self.random_mutation_min_val = random_mutation_min_val

self.random_mutation_max_val = random_mutation_max_val

# Validate that gene_constraint is a list or tuple and every element inside it is either None or callable.

if gene_constraint:

if type(gene_constraint) in [list, tuple]:

if len(gene_constraint) == self.num_genes:

for constraint_idx, item in enumerate(gene_constraint):

# Check whether the element is None or a callable.

if item is None:

pass

elif item and callable(item):

if item.__code__.co_argcount == 2:

# Every callable is valid if it receives 2 arguments.

# The 2 arguments: 1) solution 2) A list or numpy.ndarray of values to check if they meet the constraint.

pass

else:

self.valid_parameters = False

raise ValueError(f"Every callable inside the gene_constraint parameter must accept 2 arguments representing 1) The solution/chromosome where the gene exists 2) A list of NumPy array of values to check if they meet the constraint. But the callable at index {constraint_idx} named '{item.__code__.co_name}' accepts {item.__code__.co_argcount} argument(s).")

else:

self.valid_parameters = False

raise TypeError(f"The expected type of an element in the 'gene_constraint' parameter is None or a callable (e.g. function). But {item} at index {constraint_idx} of type {type(item)} found.")

else:

self.valid_parameters = False

raise ValueError(f"The number of constrains ({len(gene_constraint)}) in the 'gene_constraint' parameter must be equal to the number of genes ({self.num_genes}).")

else:

self.valid_parameters = False

raise TypeError(f"The expected type of the 'gene_constraint' parameter is either a list or tuple. But the value {gene_constraint} of type {type(gene_constraint)} found.")

else:

# gene_constraint is None and not used.

pass

self.gene_constraint = gene_constraint

# Validating the number of parents to be selected for mating (num_parents_mating)

if num_parents_mating <= 0:

self.valid_parameters = False

raise ValueError(f"The number of parents mating (num_parents_mating) parameter must be > 0 but ({num_parents_mating}) found. \nThe following parameters must be > 0: \n1) Population size (i.e. number of solutions per population) (sol_per_pop).\n2) Number of selected parents in the mating pool (num_parents_mating).\n")

# Validating the number of parents to be selected for mating: num_parents_mating

if num_parents_mating > self.sol_per_pop:

self.valid_parameters = False

raise ValueError(f"The number of parents to select for mating ({num_parents_mating}) cannot be greater than the number of solutions in the population ({self.sol_per_pop}) (i.e., num_parents_mating must always be <= sol_per_pop).\n")

self.num_parents_mating = num_parents_mating

# crossover: Refers to the method that applies the crossover operator based on the selected type of crossover in the crossover_type property.

# Validating the crossover type: crossover_type

if crossover_type is None:

self.crossover = None

elif inspect.ismethod(crossover_type):

# Check if the crossover_type is a method that accepts 3 parameters.

if len(inspect.signature(crossover_type).parameters) == 3:

# The crossover method assigned to the crossover_type parameter is validated.

self.crossover = crossover_type

else:

self.valid_parameters = False

raise ValueError(f"When 'crossover_type' is assigned to a method, then this crossover method must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.\n3) The instance from the pygad.GA class.\n\nThe passed crossover method named '{crossover_type.__code__.co_name}' accepts {len(inspect.signature(crossover_type).parameters)} parameter(s).")

elif inspect.isfunction(crossover_type):

# Check if the crossover_type is a function that accepts 3 parameters.

if len(inspect.signature(crossover_type).parameters) == 3:

# The crossover function assigned to the crossover_type parameter is validated.

self.crossover = crossover_type

else:

self.valid_parameters = False

raise ValueError(f"When 'crossover_type' is assigned to a function, then this crossover function must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed crossover function named '{crossover_type.__code__.co_name}' accepts {len(inspect.signature(crossover_type).parameters)} parameter(s).")

elif callable(crossover_type) and not inspect.isclass(crossover_type):

# The object must have the __call__() method.

if hasattr(crossover_type, '__call__'):

# Check if the __call__() method accepts 3 parameters.

if len(inspect.signature(crossover_type).parameters) == 3:

# The crossover class instance assigned to the crossover_type parameter is validated.

self.crossover = crossover_type

else:

self.valid_parameters = False

raise ValueError(f"When 'crossover_type' is assigned a class instance, then its __call__ method must accept 3 parameters:\n1) The selected parents.\n2) The size of the offspring to be produced.\n3) The instance from the pygad.GA class.\n\nThe passed instance of the class named '{crossover_type.__class__.__name__}' accepts {len(inspect.signature(crossover_type).parameters)} parameter(s).")

else:

self.valid_parameters = False

raise ValueError("When 'crossover_type' is assigned a class instance, then its __call__ method must be implemented and accept 3 parameters.")

elif not (type(crossover_type) is str):

self.valid_parameters = False

raise TypeError(f"The expected type of the 'crossover_type' parameter is either callable or str but {type(crossover_type)} found.")

else: # type crossover_type is str

crossover_type = crossover_type.lower()

if crossover_type == "single_point":

self.crossover = self.single_point_crossover

elif crossover_type == "two_points":

self.crossover = self.two_points_crossover

elif crossover_type == "uniform":

self.crossover = self.uniform_crossover

elif crossover_type == "scattered":

self.crossover = self.scattered_crossover

else:

self.valid_parameters = False

raise TypeError(f"Undefined crossover type. \nThe assigned value to the crossover_type ({crossover_type}) parameter does not refer to one of the supported crossover types which are: \n-single_point (for single point crossover)\n-two_points (for two points crossover)\n-uniform (for uniform crossover)\n-scattered (for scattered crossover).\n")

self.crossover_type = crossover_type

# Calculate the value of crossover_probability

if crossover_probability is None:

self.crossover_probability = None

elif type(crossover_probability) in self.supported_int_float_types:

if 0 <= crossover_probability <= 1:

self.crossover_probability = crossover_probability

else:

self.valid_parameters = False

raise ValueError(f"The value assigned to the 'crossover_probability' parameter must be between 0 and 1 inclusive but ({crossover_probability}) found.")

else:

self.valid_parameters = False

raise TypeError(f"Unexpected type for the 'crossover_probability' parameter. Float is expected but ({crossover_probability}) of type {type(crossover_probability)} found.")

# mutation: Refers to the method that applies the mutation operator based on the selected type of mutation in the mutation_type property.

# Validating the mutation type: mutation_type

# "adaptive" mutation is supported starting from PyGAD 2.10.0

if mutation_type is None:

self.mutation = None

elif inspect.ismethod(mutation_type):

# Check if the mutation_type is a method that accepts 2 parameters.

if (len(inspect.signature(mutation_type).parameters) == 2):

# The mutation method assigned to the mutation_type parameter is validated.

self.mutation = mutation_type

else:

self.valid_parameters = False

raise ValueError(f"When 'mutation_type' is assigned to a method, then it must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class.\n\nThe passed mutation method named '{mutation_type.__code__.co_name}' accepts {len(inspect.signature(mutation_type).parameters)} parameter(s).")

elif inspect.isfunction(mutation_type):

# Check if the mutation_type is a function that accepts 2 parameters.

if (len(inspect.signature(mutation_type).parameters) == 2):

# The mutation function assigned to the mutation_type parameter is validated.

self.mutation = mutation_type

else:

self.valid_parameters = False

raise ValueError(f"When 'mutation_type' is assigned to a function, then this mutation function must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed mutation function named '{mutation_type.__code__.co_name}' accepts {len(inspect.signature(mutation_type).parameters)} parameter(s).")

elif callable(mutation_type) and not inspect.isclass(mutation_type):

# The object must have the __call__() method.

if hasattr(mutation_type, '__call__'):

# Check if the __call__() method accepts 2 parameters.

if len(inspect.signature(mutation_type).parameters) == 2:

# The mutation class instance assigned to the mutation_type parameter is validated.

self.mutation = mutation_type

else:

self.valid_parameters = False

raise ValueError(f"When 'mutation_type' is assigned a class instance, then its __call__ method must accept 2 parameters:\n1) The offspring to be mutated.\n2) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed instance of the class named '{mutation_type.__class__.__name__}' accepts {len(inspect.signature(mutation_type).parameters)} parameter(s).")

else:

self.valid_parameters = False

raise ValueError("When 'mutation_type' is assigned a class instance, then its __call__ method must be implemented and accept 2 parameters.")

elif not (type(mutation_type) is str):

self.valid_parameters = False

raise TypeError(f"The expected type of the 'mutation_type' parameter is either callable or str but {type(mutation_type)} found.")

else: # type mutation_type is str

mutation_type = mutation_type.lower()

if mutation_type == "random":

self.mutation = self.random_mutation

elif mutation_type == "swap":

self.mutation = self.swap_mutation

elif mutation_type == "scramble":

self.mutation = self.scramble_mutation

elif mutation_type == "inversion":

self.mutation = self.inversion_mutation

elif mutation_type == "adaptive":

self.mutation = self.adaptive_mutation

else:

self.valid_parameters = False

raise TypeError(f"Undefined mutation type. \nThe assigned string value to the 'mutation_type' parameter ({mutation_type}) does not refer to one of the supported mutation types which are: \n-random (for random mutation)\n-swap (for swap mutation)\n-inversion (for inversion mutation)\n-scramble (for scramble mutation)\n-adaptive (for adaptive mutation).\n")

self.mutation_type = mutation_type

# Calculate the value of mutation_probability

if not (self.mutation_type is None):

if mutation_probability is None:

self.mutation_probability = None

elif mutation_type != "adaptive":

# Mutation probability is fixed not adaptive.

if type(mutation_probability) in self.supported_int_float_types:

if 0 <= mutation_probability <= 1:

self.mutation_probability = mutation_probability

else:

self.valid_parameters = False

raise ValueError(f"The value assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({mutation_probability}) found.")

else:

self.valid_parameters = False

raise TypeError(f"Unexpected type for the 'mutation_probability' parameter. A numeric value is expected but ({mutation_probability}) of type {type(mutation_probability)} found.")

else:

# Mutation probability is adaptive not fixed.

if type(mutation_probability) in [list, tuple, numpy.ndarray]:

if len(mutation_probability) == 2:

for el in mutation_probability:

if type(el) in self.supported_int_float_types:

if 0 <= el <= 1:

pass

else:

self.valid_parameters = False

raise ValueError(f"The values assigned to the 'mutation_probability' parameter must be between 0 and 1 inclusive but ({el}) found.")

else:

self.valid_parameters = False

raise TypeError(f"Unexpected type for a value assigned to the 'mutation_probability' parameter. A numeric value is expected but ({el}) of type {type(el)} found.")

if mutation_probability[0] < mutation_probability[1]:

if not self.suppress_warnings:

warnings.warn(f"The first element in the 'mutation_probability' parameter is {mutation_probability[0]} which is smaller than the second element {mutation_probability[1]}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high quality solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.")

self.mutation_probability = mutation_probability

else:

self.valid_parameters = False

raise ValueError(f"When mutation_type='adaptive', then the 'mutation_probability' parameter must have only 2 elements but ({len(mutation_probability)}) element(s) found.")

else:

self.valid_parameters = False

raise TypeError(f"Unexpected type for the 'mutation_probability' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_probability}) of type {type(mutation_probability)} found.")

else:

pass

# Calculate the value of mutation_num_genes

if not (self.mutation_type is None):

if mutation_num_genes is None:

# The mutation_num_genes parameter does not exist. Checking whether adaptive mutation is used.

if mutation_type != "adaptive":

# The percent of genes to mutate is fixed not adaptive.

if mutation_percent_genes == 'default'.lower():

mutation_percent_genes = 10

# Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated.

mutation_num_genes = numpy.uint32(

(mutation_percent_genes*self.num_genes)/100)

# Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1.

if mutation_num_genes == 0:

if self.mutation_probability is None:

if not self.suppress_warnings:

warnings.warn(

f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resulted in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.")

mutation_num_genes = 1

elif type(mutation_percent_genes) in self.supported_int_float_types:

if mutation_percent_genes <= 0 or mutation_percent_genes > 100:

self.valid_parameters = False

raise ValueError(f"The percentage of selected genes for mutation (mutation_percent_genes) must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n")

else:

# If mutation_percent_genes equals the string "default", then it is replaced by the numeric value 10.

if mutation_percent_genes == 'default'.lower():

mutation_percent_genes = 10

# Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated.

mutation_num_genes = numpy.uint32(

(mutation_percent_genes*self.num_genes)/100)

# Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1.

if mutation_num_genes == 0:

if self.mutation_probability is None:

if not self.suppress_warnings:

warnings.warn(f"The percentage of genes to mutate (mutation_percent_genes={mutation_percent_genes}) resulted in selecting ({mutation_num_genes}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.")

mutation_num_genes = 1

else:

self.valid_parameters = False

raise TypeError(f"Unexpected value or type of the 'mutation_percent_genes' parameter. It only accepts the string 'default' or a numeric value but ({mutation_percent_genes}) of type {type(mutation_percent_genes)} found.")

else:

# The percent of genes to mutate is adaptive not fixed.

if type(mutation_percent_genes) in [list, tuple, numpy.ndarray]:

if len(mutation_percent_genes) == 2:

mutation_num_genes = numpy.zeros_like(

mutation_percent_genes, dtype=numpy.uint32)

for idx, el in enumerate(mutation_percent_genes):

if type(el) in self.supported_int_float_types:

if el <= 0 or el > 100:

self.valid_parameters = False

raise ValueError(f"The values assigned to the 'mutation_percent_genes' must be > 0 and <= 100 but ({mutation_percent_genes}) found.\n")

else:

self.valid_parameters = False

raise TypeError(f"Unexpected type for a value assigned to the 'mutation_percent_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.")

# At this point of the loop, the current value assigned to the parameter 'mutation_percent_genes' is validated.

# Based on the mutation percentage in the 'mutation_percent_genes' parameter, the number of genes to mutate is calculated.

mutation_num_genes[idx] = numpy.uint32(

(mutation_percent_genes[idx]*self.num_genes)/100)

# Based on the mutation percentage of genes, if the number of selected genes for mutation is less than the least possible value which is 1, then the number will be set to 1.

if mutation_num_genes[idx] == 0:

if not self.suppress_warnings:

warnings.warn(f"The percentage of genes to mutate ({mutation_percent_genes[idx]}) resulted in selecting ({mutation_num_genes[idx]}) genes. The number of genes to mutate is set to 1 (mutation_num_genes=1).\nIf you do not want to mutate any gene, please set mutation_type=None.")

mutation_num_genes[idx] = 1

if mutation_percent_genes[0] < mutation_percent_genes[1]:

if not self.suppress_warnings:

warnings.warn(f"The first element in the 'mutation_percent_genes' parameter is ({mutation_percent_genes[0]}) which is smaller than the second element ({mutation_percent_genes[1]}).\nThis means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high quality solutions while making little changes in the low quality solutions.\nPlease make the first element higher than the second element.")

# At this point outside the loop, all values of the parameter 'mutation_percent_genes' are validated. Everything is OK.

else:

self.valid_parameters = False

raise ValueError(f"When mutation_type='adaptive', then the 'mutation_percent_genes' parameter must have only 2 elements but ({len(mutation_percent_genes)}) element(s) found.")

else:

if self.mutation_probability is None:

self.valid_parameters = False

raise TypeError(f"Unexpected type of the 'mutation_percent_genes' parameter. When mutation_type='adaptive', then the 'mutation_percent_genes' parameter should exist and assigned a list/tuple/numpy.ndarray with 2 values but ({mutation_percent_genes}) found.")

# The mutation_num_genes parameter exists. Checking whether adaptive mutation is used.

elif mutation_type != "adaptive":

# Number of genes to mutate is fixed not adaptive.

if type(mutation_num_genes) in self.supported_int_types:

if mutation_num_genes <= 0:

self.valid_parameters = False

raise ValueError(f"The number of selected genes for mutation (mutation_num_genes) cannot be <= 0 but ({mutation_num_genes}) found. If you do not want to use mutation, please set mutation_type=None\n")

elif mutation_num_genes > self.num_genes:

self.valid_parameters = False

raise ValueError(f"The number of selected genes for mutation (mutation_num_genes), which is ({mutation_num_genes}), cannot be greater than the number of genes ({self.num_genes}).\n")

else:

self.valid_parameters = False

raise TypeError(f"The 'mutation_num_genes' parameter is expected to be a positive integer but the value ({mutation_num_genes}) of type {type(mutation_num_genes)} found.\n")

else:

# Number of genes to mutate is adaptive not fixed.

if type(mutation_num_genes) in [list, tuple, numpy.ndarray]:

if len(mutation_num_genes) == 2:

for el in mutation_num_genes:

if type(el) in self.supported_int_types:

if el <= 0:

self.valid_parameters = False

raise ValueError(f"The values assigned to the 'mutation_num_genes' cannot be <= 0 but ({el}) found. If you do not want to use mutation, please set mutation_type=None\n")

elif el > self.num_genes:

self.valid_parameters = False

raise ValueError(f"The values assigned to the 'mutation_num_genes' cannot be greater than the number of genes ({self.num_genes}) but ({el}) found.\n")

else:

self.valid_parameters = False

raise TypeError(f"Unexpected type for a value assigned to the 'mutation_num_genes' parameter. An integer value is expected but ({el}) of type {type(el)} found.")

# At this point of the loop, the current value assigned to the parameter 'mutation_num_genes' is validated.

if mutation_num_genes[0] < mutation_num_genes[1]:

if not self.suppress_warnings:

warnings.warn(f"The first element in the 'mutation_num_genes' parameter is {mutation_num_genes[0]} which is smaller than the second element {mutation_num_genes[1]}. This means the mutation rate for the high-quality solutions is higher than the mutation rate of the low-quality ones. This causes high disruption in the high quality solutions while making little changes in the low quality solutions. Please make the first element higher than the second element.")

# At this point outside the loop, all values of the parameter 'mutation_num_genes' are validated. Everything is OK.

else:

self.valid_parameters = False

raise ValueError(f"When mutation_type='adaptive', then the 'mutation_num_genes' parameter must have only 2 elements but ({len(mutation_num_genes)}) element(s) found.")

else:

self.valid_parameters = False

raise TypeError(f"Unexpected type for the 'mutation_num_genes' parameter. When mutation_type='adaptive', then list/tuple/numpy.ndarray is expected but ({mutation_num_genes}) of type {type(mutation_num_genes)} found.")

else:

pass

# Validating mutation_by_replacement and mutation_type

if self.mutation_type != "random" and self.mutation_by_replacement:

if not self.suppress_warnings:

warnings.warn(f"The mutation_by_replacement parameter is set to True while the mutation_type parameter is not set to random but ({mutation_type}). Note that the mutation_by_replacement parameter has an effect only when mutation_type='random'.")

# Check if crossover and mutation are both disabled.

if (self.mutation_type is None) and (self.crossover_type is None):

if not self.suppress_warnings:

warnings.warn("The 2 parameters mutation_type and crossover_type are None. This disables any type of evolution the genetic algorithm can make. As a result, the genetic algorithm cannot find a better solution that the best solution in the initial population.")

# select_parents: Refers to a method that selects the parents based on the parent selection type specified in the parent_selection_type attribute.

# Validating the selected type of parent selection: parent_selection_type

if inspect.ismethod(parent_selection_type):

# Check if the parent_selection_type is a method that accepts 3 parameters.

if len(inspect.signature(parent_selection_type).parameters) == 3:

# The parent selection method assigned to the parent_selection_type parameter is validated.

self.select_parents = parent_selection_type

else:

self.valid_parameters = False

raise ValueError(f"When 'parent_selection_type' is assigned to a method, then it must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class.\n\nThe passed parent selection method named '{parent_selection_type.__code__.co_name}' accepts {len(inspect.signature(parent_selection_type).parameters)} parameter(s).")

elif inspect.isfunction(parent_selection_type):

# Check if the parent_selection_type is a function that accepts 2 parameters.

if len(inspect.signature(parent_selection_type).parameters) == 3:

# The parent selection function assigned to the parent_selection_type parameter is validated.

self.select_parents = parent_selection_type

else:

self.valid_parameters = False

raise ValueError(f"When 'parent_selection_type' is assigned to a user-defined function, then this parent selection function must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed parent selection function named '{parent_selection_type.__code__.co_name}' accepts {len(inspect.signature(parent_selection_type).parameters)} parameter(s).")

elif callable(parent_selection_type) and not inspect.isclass(parent_selection_type):

# The object must have the __call__() method.

if hasattr(parent_selection_type, '__call__'):

# Check if the __call__() method accepts 3 parameters.

if len(inspect.signature(parent_selection_type).parameters) == 3:

# The parent selection class instance assigned to the parent_selection_type parameter is validated.

self.select_parents = parent_selection_type

else:

self.valid_parameters = False

raise ValueError(f"When 'parent_selection_type' is assigned a class instance, then its __call__ method must accept 3 parameters:\n1) The fitness values of the current population.\n2) The number of parents needed.\n3) The instance from the pygad.GA class to retrieve any property like population, gene data type, gene space, etc.\n\nThe passed instance of the class named '{parent_selection_type.__class__.__name__}' accepts {len(inspect.signature(parent_selection_type).parameters)} parameter(s).")

else:

self.valid_parameters = False

raise ValueError("When 'parent_selection_type' is assigned a class instance, then its __call__ method must be implemented and accept 3 parameters.")

elif not (type(parent_selection_type) is str):

self.valid_parameters = False

raise TypeError(f"The expected type of the 'parent_selection_type' parameter is either callable or str but {type(parent_selection_type)} found.")

else:

parent_selection_type = parent_selection_type.lower()

if parent_selection_type == "sss":

self.select_parents = self.steady_state_selection

elif parent_selection_type == "rws":

self.select_parents = self.roulette_wheel_selection

elif parent_selection_type == "sus":

self.select_parents = self.stochastic_universal_selection

elif parent_selection_type == "random":

self.select_parents = self.random_selection

elif parent_selection_type == "tournament":

self.select_parents = self.tournament_selection

elif parent_selection_type == "tournament_nsga2": # Supported in PyGAD >= 3.2

self.select_parents = self.tournament_selection_nsga2

elif parent_selection_type == "nsga2": # Supported in PyGAD >= 3.2

self.select_parents = self.nsga2_selection

elif parent_selection_type == "rank":

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 (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":

if type(K_tournament) in self.supported_int_types:

if K_tournament > self.sol_per_pop:

K_tournament = self.sol_per_pop

if not self.suppress_warnings:

warnings.warn(f"K of the tournament selection ({K_tournament}) should not be greater than the number of solutions within the population ({self.sol_per_pop}).\nK will be clipped to be equal to the number of solutions in the population (sol_per_pop).\n")

elif K_tournament <= 0:

self.valid_parameters = False

raise ValueError(f"K of the tournament selection cannot be <=0 but ({K_tournament}) found.\n")

else:

self.valid_parameters = False

raise ValueError(f"The type of K of the tournament selection must be integer but the value ({K_tournament}) of type ({type(K_tournament)}) found.")

self.K_tournament = K_tournament

# Validating the number of parents to keep in the next population: keep_parents

if not (type(keep_parents) in self.supported_int_types):

self.valid_parameters = False

raise TypeError(f"Incorrect type of the value assigned to the keep_parents parameter. The value ({keep_parents}) of type {type(keep_parents)} found but an integer is expected.")

elif keep_parents > self.sol_per_pop or keep_parents > self.num_parents_mating or keep_parents < -1:

self.valid_parameters = False

raise ValueError(f"Incorrect value to the keep_parents parameter: {keep_parents}. \nThe assigned value to the keep_parent parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Less than or equal to num_parents_mating\n3) Greater than or equal to -1.")

self.keep_parents = keep_parents

if parent_selection_type == "sss" and self.keep_parents == 0:

if not self.suppress_warnings:

warnings.warn("The steady-state parent (sss) selection operator is used despite that no parents are kept in the next generation.")

# Validating the number of elitism to keep in the next population: keep_elitism

if not (type(keep_elitism) in self.supported_int_types):

self.valid_parameters = False

raise TypeError(f"Incorrect type of the value assigned to the keep_elitism parameter. The value ({keep_elitism}) of type {type(keep_elitism)} found but an integer is expected.")

elif keep_elitism > self.sol_per_pop or keep_elitism < 0:

self.valid_parameters = False

raise ValueError(f"Incorrect value to the keep_elitism parameter: {keep_elitism}. \nThe assigned value to the keep_elitism parameter must satisfy the following conditions: \n1) Less than or equal to sol_per_pop\n2) Greater than or equal to 0.")

self.keep_elitism = keep_elitism

# Validate keep_parents.

if self.keep_elitism == 0:

# Keep all parents in the next population.

if self.keep_parents == -1:

self.num_offspring = self.sol_per_pop - self.num_parents_mating

# Keep no parents in the next population.

elif self.keep_parents == 0:

self.num_offspring = self.sol_per_pop

# Keep the specified number of parents in the next population.

elif self.keep_parents > 0:

self.num_offspring = self.sol_per_pop - self.keep_parents

else:

self.num_offspring = self.sol_per_pop - self.keep_elitism

# Check if the fitness_func is a method.

if inspect.ismethod(fitness_func):

# Check if the fitness method accepts 3 parameters.

if len(inspect.signature(fitness_func).parameters) == 3:

self.fitness_func = fitness_func

else:

self.valid_parameters = False

raise ValueError(f"In PyGAD 2.20.0, if a method is used to calculate the fitness value, then it must accept 3 parameters\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness method named '{fitness_func.__code__.co_name}' accepts {len(inspect.signature(fitness_func).parameters)} parameter(s).")

elif inspect.isfunction(fitness_func):

# Check if the fitness function accepts 3 parameters.

if len(inspect.signature(fitness_func).parameters) == 3:

self.fitness_func = fitness_func

else:

self.valid_parameters = False

raise ValueError(f"In PyGAD 2.20.0, the fitness function must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed fitness function named '{fitness_func.__code__.co_name}' accepts {len(inspect.signature(fitness_func).parameters)} parameter(s).")

elif callable(fitness_func) and not inspect.isclass(fitness_func):

# The object must have the __call__() method.

if hasattr(fitness_func, '__call__'):

# Check if the __call__() method accepts 3 parameters.

if len(inspect.signature(fitness_func).parameters) == 3:

# The fitness class instance assigned to the fitness_func parameter is validated.

self.fitness_func = fitness_func

else:

self.valid_parameters = False

raise ValueError(f"When 'fitness_func' is assigned a class instance, then its __call__ method must accept 3 parameters:\n1) The instance of the 'pygad.GA' class.\n2) A solution to calculate its fitness value.\n3) The solution's index within the population.\n\nThe passed instance of the class named '{fitness_func.__class__.__name__}' accepts {len(inspect.signature(fitness_func).parameters)} parameter(s).")

else:

self.valid_parameters = False

raise ValueError("When 'fitness_func' is assigned a class instance, then its __call__ method must be implemented and accept 3 parameters.")