#include <genetic_algorithm.h>

Inheritance diagram for GeneticAlgorithm:

Public Member Functions
	GeneticAlgorithm ()
	Default constructor. More...

	GeneticAlgorithm (TrainingStrategy *)

virtual	~GeneticAlgorithm ()
	Destructor. More...

const Tensor< bool, 2 > &	get_population () const
	Returns the population matrix. More...

const Tensor< type, 1 > &	get_fitness () const
	Returns the fitness of the population. More...

const Tensor< bool, 1 > &	get_selection () const

Index	get_individuals_number () const
	Returns the size of the population. More...

Index	get_genes_number () const

const type &	get_mutation_rate () const
	Returns the rate used in the mutation. More...

const Index &	get_elitism_size () const
	Returns the size of the elite in the selection. More...

void	set_default ()
	Sets the members of the genetic algorithm object to their default values. More...

void	set_population (const Tensor< bool, 2 > &)

void	set_individuals_number (const Index &)

void	set_training_errors (const Tensor< type, 1 > &)

void	set_selection_errors (const Tensor< type, 1 > &)

void	set_fitness (const Tensor< type, 1 > &)

void	set_mutation_rate (const type &)

void	set_elitism_size (const Index &)

void	initialize_population ()
	Initialize the population depending on the intialization method. More...

void	evaluate_population ()

void	perform_fitness_assignment ()
	Calculate the fitness with the errors depending on the fitness assignment method. More...

void	perform_selection ()
	Selects for crossover some individuals from the population. More...

void	perform_crossover ()
	Perform the crossover depending on the crossover method. More...

void	perform_mutation ()
	Perform the mutation of the individuals generated in the crossover. More...

InputsSelectionResults	perform_inputs_selection ()
	Select the inputs with best generalization properties using the genetic algorithm. More...

Tensor< string, 2 >	to_string_matrix () const

void	from_XML (const tinyxml2::XMLDocument &)

void	write_XML (tinyxml2::XMLPrinter &) const

void	print () const

void	save (const string &) const

void	load (const string &)

Public Member Functions inherited from InputsSelection
	InputsSelection ()
	Default constructor. More...

	InputsSelection (TrainingStrategy *)

virtual	~InputsSelection ()
	Destructor. More...

TrainingStrategy *	get_training_strategy_pointer () const
	Returns a pointer to the training strategy object. More...

bool	has_training_strategy () const
	Returns true if this inputs selection algorithm has a training strategy associated, and false otherwise. More...

const Index &	get_trials_number () const
	Returns the number of trials for each network architecture. More...

const bool &	get_display () const

const type &	get_selection_error_goal () const
	Returns the goal for the selection error in the inputs selection algorithm. More...

const Index &	get_maximum_iterations_number () const
	Returns the maximum number of iterations in the inputs selection algorithm. More...

const type &	get_maximum_time () const
	Returns the maximum time in the inputs selection algorithm. More...

const type &	get_maximum_correlation () const
	Return the maximum correlation for the algorithm. More...

const type &	get_minimum_correlation () const
	Return the minimum correlation for the algorithm. More...

const type &	get_tolerance () const

void	set (TrainingStrategy *)

void	set_default ()
	Sets the members of the inputs selection object to their default values. More...

void	set_trials_number (const Index &)

void	set_display (const bool &)

void	set_selection_error_goal (const type &)

void	set_maximum_epochs_number (const Index &)

void	set_maximum_time (const type &)

void	set_maximum_correlation (const type &)

void	set_minimum_correlation (const type &)

string	write_stopping_condition (const TrainingResults &) const

void	check () const
	Checks that the different pointers needed for performing the inputs selection are not nullptr. More...

Index	get_input_index (const Tensor< DataSet::VariableUse, 1 > &, const Index &)

const string	write_time (const type &) const
	Writes the time from seconds in format HH:mm:ss. More...

Private Attributes
Tensor< bool, 2 >	population
	Population matrix. More...

Tensor< type, 1 >	fitness
	Fitness of population. More...

Tensor< bool, 1 >	selection

Tensor< Tensor< type, 1 >, 1 >	parameters
	Performance of population. More...

Tensor< type, 1 >	training_errors

Tensor< type, 1 >	selection_errors

type	mutation_rate

Index	elitism_size

Additional Inherited Members
Public Types inherited from InputsSelection
enum class	StoppingCondition { MaximumTime , SelectionErrorGoal , MaximumInputs , MinimumInputs , MaximumEpochs , MaximumSelectionFailures , CorrelationGoal }
	Enumeration of all possibles condition of stop for the algorithms. More...

Protected Attributes inherited from InputsSelection
TrainingStrategy *	training_strategy_pointer = nullptr
	Pointer to a training strategy object. More...

Tensor< Index, 1 >	original_input_columns_indices

Tensor< Index, 1 >	original_target_columns_indices

Index	trials_number = 1
	Number of trials for each neural network. More...

bool	display = true
	Display messages to screen. More...

type	selection_error_goal
	Goal value for the selection error. It is used as a stopping criterion. More...

Index	maximum_epochs_number
	Maximum number of epochs to perform_inputs_selection. It is used as a stopping criterion. More...

type	maximum_correlation
	Maximum value for the correlations. More...

type	minimum_correlation
	Minimum value for the correlations. More...

type	maximum_time
	Maximum selection algorithm time. It is used as a stopping criterion. More...

const Eigen::array< int, 1 >	rows_sum = {Eigen::array<int, 1>({1})}

Detailed Description

This concrete class represents a genetic algorithm, inspired by the process of natural selection[1] such as mutation, crossover and selection. This algorithm are commonly used in optimization and search problems. if the data_set has many inputs, but we do not know how they affect the target, then this algorithm provides the best possible combination of variables to optimize the problem.

[1] Neural Designer "Genetic Algorithms for Feature Selection." https://www.neuraldesigner.com/blog/genetic_algorithms_for_feature_selection

Definition at line 43 of file genetic_algorithm.h.

Constructor & Destructor Documentation

◆ GeneticAlgorithm() [1/2]

GeneticAlgorithm ( )

explicit

Default constructor.

Definition at line 16 of file genetic_algorithm.cpp.

◆ GeneticAlgorithm() [2/2]

GeneticAlgorithm ( TrainingStrategy * new_training_strategy_pointer )

explicit

Training strategy constructor.

Parameters

new_training_strategy_pointer Pointer to a training strategy object.

Definition at line 26 of file genetic_algorithm.cpp.

◆ ~GeneticAlgorithm()

~GeneticAlgorithm ( )

virtual

Destructor.

Definition at line 35 of file genetic_algorithm.cpp.

Member Function Documentation

◆ evaluate_population()

void evaluate_population ( )

Evaluate the population loss. Training all the neural networks in the population and calculate their fitness.

Definition at line 386 of file genetic_algorithm.cpp.

◆ from_XML()

void from_XML ( const tinyxml2::XMLDocument & document )

Deserializes a TinyXML document into this genetic algorithm object.

Parameters

document TinyXML document containing the member data.

Definition at line 1079 of file genetic_algorithm.cpp.

◆ get_elitism_size()

const Index & get_elitism_size ( ) const

Returns the size of the elite in the selection.

Definition at line 86 of file genetic_algorithm.cpp.

◆ get_fitness()

const Tensor< type, 1 > & get_fitness ( ) const

Returns the fitness of the population.

Definition at line 50 of file genetic_algorithm.cpp.

◆ get_genes_number()

Index get_genes_number ( ) const

Definition at line 70 of file genetic_algorithm.cpp.

◆ get_individuals_number()

Index get_individuals_number ( ) const

Returns the size of the population.

Definition at line 64 of file genetic_algorithm.cpp.

◆ get_mutation_rate()

const type & get_mutation_rate ( ) const

Returns the rate used in the mutation.

Definition at line 78 of file genetic_algorithm.cpp.

◆ get_population()

const Tensor< bool, 2 > & get_population ( ) const

Returns the population matrix.

Definition at line 42 of file genetic_algorithm.cpp.

◆ get_selection()

const Tensor< bool, 1 > & get_selection ( ) const

Definition at line 56 of file genetic_algorithm.cpp.

◆ initialize_population()

void initialize_population ( )

Initialize the population depending on the intialization method.

Definition at line 338 of file genetic_algorithm.cpp.

◆ load()

void load ( const string & file_name )

Loads a genetic algorithm object from a XML-type file.

Parameters

file_name Name of genetic algorithm XML-type file.

Definition at line 1293 of file genetic_algorithm.cpp.

◆ perform_crossover()

void perform_crossover ( )

Perform the crossover depending on the crossover method.

Definition at line 594 of file genetic_algorithm.cpp.

◆ perform_fitness_assignment()

void perform_fitness_assignment ( )

Calculate the fitness with the errors depending on the fitness assignment method.

Definition at line 489 of file genetic_algorithm.cpp.

◆ perform_inputs_selection()

InputsSelectionResults perform_inputs_selection ( )

virtual

Select the inputs with best generalization properties using the genetic algorithm.

Implements InputsSelection.

Definition at line 775 of file genetic_algorithm.cpp.

◆ perform_mutation()

void perform_mutation ( )

Perform the mutation of the individuals generated in the crossover.

Definition at line 742 of file genetic_algorithm.cpp.

◆ perform_selection()

void perform_selection ( )

Selects for crossover some individuals from the population.

Definition at line 504 of file genetic_algorithm.cpp.

◆ print()

void print ( ) const

Definition at line 1267 of file genetic_algorithm.cpp.

◆ save()

void save ( const string & file_name ) const

Saves to a XML-type file the members of the genetic algorithm object.

Parameters

file_name Name of genetic algorithm XML-type file.

Definition at line 1278 of file genetic_algorithm.cpp.

◆ set_default()

void set_default ( )

Sets the members of the genetic algorithm object to their default values.

Definition at line 94 of file genetic_algorithm.cpp.

◆ set_elitism_size()

void set_elitism_size ( const Index & new_elitism_size )

Sets the number of individuals with the greatest fitness selected.

Parameters

new_elitism_size Size of the elitism.

Definition at line 312 of file genetic_algorithm.cpp.

◆ set_fitness()

void set_fitness ( const Tensor< type, 1 > & new_fitness )

Sets a new fitness for the population.

Parameters

new_fitness New fitness values.

Definition at line 208 of file genetic_algorithm.cpp.

◆ set_individuals_number()

void set_individuals_number ( const Index & new_individuals_number )

Sets a new population size. It must be greater than 4.

Parameters

new_population_size Size of the population.

Definition at line 249 of file genetic_algorithm.cpp.

◆ set_mutation_rate()

void set_mutation_rate ( const type & new_mutation_rate )

Sets a new rate used in the mutation. It is a number between 0 and 1.

Parameters

new_mutation_rate Rate used for the mutation.

Definition at line 288 of file genetic_algorithm.cpp.

◆ set_population()

void set_population ( const Tensor< bool, 2 > & new_population )

Sets a new popualtion.

Parameters

new_population New population matrix.

Definition at line 129 of file genetic_algorithm.cpp.

◆ set_selection_errors()

void set_selection_errors ( const Tensor< type, 1 > & new_selection_errors )

Definition at line 199 of file genetic_algorithm.cpp.

◆ set_training_errors()

void set_training_errors ( const Tensor< type, 1 > & new_training_errors )

Definition at line 193 of file genetic_algorithm.cpp.

◆ to_string_matrix()

Tensor< string, 2 > to_string_matrix ( ) const

Writes as matrix of strings the most representative atributes.

Todo:: to many rows in string matrix.

Definition at line 946 of file genetic_algorithm.cpp.

◆ write_XML()

void write_XML ( tinyxml2::XMLPrinter & file_stream ) const

Serializes the genetic algorithm object into a XML document of the TinyXML library without keep the DOM tree in memory. See the OpenNN manual for more information about the format of this document.

Definition at line 998 of file genetic_algorithm.cpp.

Member Data Documentation

◆ elitism_size

Index elitism_size

private

Elitism size. It represents the number of individuals which will always be selected for recombination. This is a parameter of the selection operator.

Definition at line 157 of file genetic_algorithm.h.

◆ fitness

Tensor<type, 1> fitness

private

Fitness of population.

Definition at line 136 of file genetic_algorithm.h.

◆ mutation_rate

type mutation_rate

private

Mutation rate. The mutation rate value must be between 0 and 1. This is a parameter of the mutation operator.

Definition at line 151 of file genetic_algorithm.h.

◆ parameters

Tensor<Tensor<type, 1>, 1> parameters

private

Performance of population.

Definition at line 142 of file genetic_algorithm.h.

◆ population

Tensor<bool, 2> population

private

Population matrix.

Definition at line 132 of file genetic_algorithm.h.

◆ selection

Tensor<bool, 1> selection

private

Definition at line 138 of file genetic_algorithm.h.

◆ selection_errors

Tensor<type, 1> selection_errors

private

Definition at line 145 of file genetic_algorithm.h.

◆ training_errors

Tensor<type, 1> training_errors

private

Definition at line 144 of file genetic_algorithm.h.

The documentation for this class was generated from the following files:

Public Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ GeneticAlgorithm() [1/2]

◆ GeneticAlgorithm() [2/2]

◆ ~GeneticAlgorithm()

Member Function Documentation

◆ evaluate_population()

◆ from_XML()

◆ get_elitism_size()

◆ get_fitness()

◆ get_genes_number()

◆ get_individuals_number()

◆ get_mutation_rate()

◆ get_population()

◆ get_selection()

◆ initialize_population()

◆ load()

◆ perform_crossover()

◆ perform_fitness_assignment()

◆ perform_inputs_selection()

◆ perform_mutation()

◆ perform_selection()

◆ print()

◆ save()

◆ set_default()

◆ set_elitism_size()

◆ set_fitness()

◆ set_individuals_number()

◆ set_mutation_rate()

◆ set_population()

◆ set_selection_errors()

◆ set_training_errors()

◆ to_string_matrix()

◆ write_XML()

Member Data Documentation

◆ elitism_size

◆ fitness

◆ mutation_rate

◆ parameters

◆ population

◆ selection

◆ selection_errors

◆ training_errors