moeoIBEAAvgSorting< EOT, FitnessEval > Class Template Reference

Functor The sorting phase of IBEA (Indicator-Based Evolutionary Algorithm) under uncertainty using averaged values for each objective Follow the idea presented in the Deb & Gupta paper "Searching for Robust Pareto-Optimal Solutions in Multi-Objective Optimization", 2005 Of course, the fitness of an individual needs to be an eoStochasticParetoFitness object. More...

#include <moeoIBEA.h>

Inheritance diagram for moeoIBEAAvgSorting< EOT, FitnessEval >:

List of all members.

Public Member Functions
	moeoIBEAAvgSorting (moeoBinaryQualityIndicator< FitnessEval > *_I, const double _kappa)
	constructor
Private Member Functions
void	setBounds (const eoPop< EOT > &_pop)
	computation and setting of the bounds for each objective
void	fitnesses (const eoPop< EOT > &_pop)
	computation and setting of the fitness for each individual of the population
Private Attributes
double	kappa
	scaling factor kappa

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Detailed Description

template<class EOT, class FitnessEval = typename EOT::Fitness::FitnessEval>
class moeoIBEAAvgSorting< EOT, FitnessEval >

Functor The sorting phase of IBEA (Indicator-Based Evolutionary Algorithm) under uncertainty using averaged values for each objective Follow the idea presented in the Deb & Gupta paper "Searching for Robust Pareto-Optimal Solutions in Multi-Objective Optimization", 2005 Of course, the fitness of an individual needs to be an eoStochasticParetoFitness object.

Definition at line 361 of file moeoIBEA.h.

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Constructor & Destructor Documentation

template<class EOT, class FitnessEval = typename EOT::Fitness::FitnessEval>

moeoIBEAAvgSorting< EOT, FitnessEval >::moeoIBEAAvgSorting	(	moeoBinaryQualityIndicator< FitnessEval > *	_I,
		const double	_kappa
	)			[inline]

constructor

Parameters:

	eoBinaryQualityIndicator<EOT>*	_I the binary quality indicator to use in the selection process
	double	_kappa scaling factor kappa

Definition at line 373 of file moeoIBEA.h.

References moeoIBEAAvgSorting< EOT, FitnessEval >::kappa.

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Member Function Documentation

template<class EOT, class FitnessEval = typename EOT::Fitness::FitnessEval>

void moeoIBEAAvgSorting< EOT, FitnessEval >::setBounds

(

const eoPop< EOT > &

_pop

)

[inline, private, virtual]

computation and setting of the bounds for each objective

Parameters:

const

eoPop<EOT>& _pop the population

Implements moeoIBEA< EOT, FitnessEval >.

Definition at line 398 of file moeoIBEA.h.

References moeoIBEA< EOT, FitnessEval >::I, and moeoBinaryQualityIndicator< EOFitness >::setBounds().

template<class EOT, class FitnessEval = typename EOT::Fitness::FitnessEval>

void moeoIBEAAvgSorting< EOT, FitnessEval >::fitnesses

(

const eoPop< EOT > &

_pop

)

[inline, private, virtual]

computation and setting of the fitness for each individual of the population

Parameters:

const

eoPop<EOT>& _pop the population

Implements moeoIBEA< EOT, FitnessEval >.

Definition at line 431 of file moeoIBEA.h.

References moeoIBEAAvgSorting< EOT, FitnessEval >::kappa, and eoValueParam< std::vector< WorthT > >::value().

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

• moeoIBEA.h

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