Self-Adaptive Fitness constraints handling meta-algorithm. More...

#include <cstrs_self_adaptive.h>

Inheritance diagram for pagmo::algorithm::cstrs_self_adaptive:

Public Member Functions
	cstrs_self_adaptive (const base &=jde(), int gen=1, double=1e-15, double=1e-15)
	Constructor. More...

	cstrs_self_adaptive (const cstrs_self_adaptive &)
	Copy constructor.

base_ptr	clone () const
	Clone method.

void	evolve (population &) const
	Evolve implementation. More...

std::string	get_name () const
	Algorithm name.

base_ptr	get_algorithm () const
	Get a copy of the internal local algorithm. More...

void	set_algorithm (const base &)
	Set algorithm. More...

Public Member Functions inherited from pagmo::algorithm::base
	base ()
	Default constructor. More...

virtual	~base ()
	Trivial destructor. More...

std::string	human_readable () const
	Return human readable representation of the algorithm. More...

void	set_screen_output (const bool p)
	Setter-Getter for protected m_screen_output data. More...

bool	get_screen_output () const
	Gets screen output. More...

void	reset_rngs (const unsigned int) const
	Resets the seed of the internal rngs using a user-provided seed. More...

Protected Member Functions
std::string	human_readable_extra () const
	Extra human readable algorithm info. More...

Friends
class	boost::serialization::access

Additional Inherited Members
Protected Attributes inherited from pagmo::algorithm::base
bool	m_screen_output
	Indicates to the derived class whether to print stuff on screen.

rng_double	m_drng
	Random number generator for double-precision floating point values.

rng_uint32	m_urng
	Random number generator for unsigned integer values.

unsigned int	m_fevals
	A counter for the number of function evaluations.

Detailed Description

Self-Adaptive Fitness constraints handling meta-algorithm.

Seld-Adaptive Fitness constraints handling is a meta-algorithm that allows to solve constrained optimization problems. The key idea of this constraint handling technique is to represent the constraint violation by a single infeasibility measure, and to adapt dynamically the penalization of infeasible solutions.

This meta-algorithm is based on the problem self-adaptive.

Note: This constraints handling technique can only be used for MINIMIZATION problems

See also: Farmani, R., & Wright, J. A. (2003). Self-adaptive fitness formulation for constrained optimization. Evolutionary Computation, IEEE Transactions on, 7(5), 445-455 for the paper introducing the method.

Author: Jeremie Labroquere (jerem.nosp@m.ie.l.nosp@m.abroq.nosp@m.uere.nosp@m.@gmai.nosp@m.l.co.nosp@m.m)

Definition at line 56 of file algorithm/cstrs_self_adaptive.h.

Constructor & Destructor Documentation

pagmo::algorithm::cstrs_self_adaptive::cstrs_self_adaptive	(	const base &	original_algo = `jde()`,
		int	gen = `1`,
		double	ftol = `1e-15`,
		double	xtol = `1e-15`
	)

Constructor.

Constructs an self adaptive algorithm.

Parameters

[in]	original_algo	pagmo::algorithm to use as 'original' optimization method
[in]	gen	number of generations.
[in]	ftol	stopping criteria on the f tolerance.
[in]	xtol	stopping criteria on the x tolerance.