ParetoGA Class Reference

Implements a genetic algorithm based on the concept of pareto-optimality, best suited for multi-objective problems. More...

#include <ParetoGA.h>

Inheritance diagram for ParetoGA:

[legend]

List of all members.

Public Member Functions

unsigned virtual int GetNBestmodels ()

Return the size of the pareto-optimal front.

virtual std::vector< int > GetBestModelIndices ()

Get the indices of the models within the Pareto front.

virtual void CalcProbabilities (const int iterationnumber, gplib::rmat &LocalMisFit, GeneralPopulation &LocalPopulation)

Calculate the probabilities of reproduction by ranking the population.

void PrintRanks (std::ostream &output)

Write the population by ranks to the stream output.

void PrintFront (std::ostream &output)

Write the models in the pareto-optimal front to stream output.

ParetoGA (GeneralPropagation *const LocalPropagation, GeneralPopulation *const LocalPopulation, GeneralTranscribe *const LocalTranscribe, const tObjectiveVector &IndObjective, const int nthreads=1)

The constructor needs other classes that determine the behaviour of the GA.

virtual ~ParetoGA ()

Protected Member Functions

virtual void Elitism (const int iterationnumber)

The function Elitism ensures that the best models are preserved after mutation and crossover.

Detailed Description

Implements a genetic algorithm based on the concept of pareto-optimality, best suited for multi-objective problems.

The class ParetoGA implements a variant of NSGA-II by Deb et al.: Deb, K., Pratap. A, Agarwal, S., and Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transaction on Evolutionary Computation, 6(2), 181-197.

Definition at line 13 of file ParetoGA.h.

Constructor & Destructor Documentation

ParetoGA::ParetoGA	(	GeneralPropagation *const	LocalPropagation,
		GeneralPopulation *const	LocalPopulation,
		GeneralTranscribe *const	LocalTranscribe,
		const tObjectiveVector &	IndObjective,
		const int	nthreads = `1`
	)			`[inline]`

The constructor needs other classes that determine the behaviour of the GA.

Definition at line 43 of file ParetoGA.h.

ParetoGA::~ParetoGA ( ) [virtual]

Definition at line 224 of file ParetoGA.cpp.

Member Function Documentation

void ParetoGA::CalcProbabilities	(	const int	iterationnumber,
		gplib::rmat &	LocalMisFit,
		GeneralPopulation &	LocalPopulation
	)			`[virtual]`

Calculate the probabilities of reproduction by ranking the population.

Implements GeneralGA.

Definition at line 82 of file ParetoGA.cpp.

References GeneralPopulation::GetPopsize(), GeneralPopulation::SetProbabilities(), and size.

Referenced by Elitism().

void ParetoGA::Elitism ( const int iterationnumber ) [protected, virtual]

The function Elitism ensures that the best models are preserved after mutation and crossover.

Reimplemented from GeneralGA.

Definition at line 154 of file ParetoGA.cpp.

References CalcProbabilities(), GeneralPopulation::GetGenesize(), GeneralPopulation::GetOldPopulation(), GeneralPopulation::GetPopsize(), GeneralPopulation::GetPopulation(), GeneralGA::MisFit, GeneralGA::OldMisFit, GeneralGA::Population, and GeneralPopulation::SetPopulation().

virtual std::vector<int> ParetoGA::GetBestModelIndices ( ) [inline, virtual]

Get the indices of the models within the Pareto front.

Implements GeneralGA.

Definition at line 31 of file ParetoGA.h.

unsigned virtual int ParetoGA::GetNBestmodels ( ) [inline, virtual]

Return the size of the pareto-optimal front.

Implements GeneralGA.

Definition at line 26 of file ParetoGA.h.

void ParetoGA::PrintFront ( std::ostream & output )

Write the models in the pareto-optimal front to stream output.

Definition at line 211 of file ParetoGA.cpp.

References GeneralGA::MisFit.

void ParetoGA::PrintRanks ( std::ostream & output )

Write the population by ranks to the stream output.

Definition at line 193 of file ParetoGA.cpp.

References GeneralGA::MisFit.

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


Public Member Functions
unsigned virtual int	GetNBestmodels ()
	Return the size of the pareto-optimal front.
virtual std::vector< int >	GetBestModelIndices ()
	Get the indices of the models within the Pareto front.
virtual void	CalcProbabilities (const int iterationnumber, gplib::rmat &LocalMisFit, GeneralPopulation &LocalPopulation)
	Calculate the probabilities of reproduction by ranking the population.
void	PrintRanks (std::ostream &output)
	Write the population by ranks to the stream output.
void	PrintFront (std::ostream &output)
	Write the models in the pareto-optimal front to stream output.
	ParetoGA (GeneralPropagation const LocalPropagation, GeneralPopulation const LocalPopulation, GeneralTranscribe *const LocalTranscribe, const tObjectiveVector &IndObjective, const int nthreads=1)
	The constructor needs other classes that determine the behaviour of the GA.
virtual	~ParetoGA ()
Protected Member Functions
virtual void	Elitism (const int iterationnumber)
	The function Elitism ensures that the best models are preserved after mutation and crossover.