Interface to a single evolutionary and genetic algorithms thread. More...

Inheritance diagram for SiMoPO.GA.IGeneticAlgorithms:

Public Member Functions
int	StartRaw (ref byte Maximization, ref int Dimension, double[] OptiPara, double[] OptiParaMin, double[] OptiParaMax, double[] OptiParaStep, byte[] OptiParaIsInvolved, byte[] OptiParaMaxIterate, double[] OptiParaOptimum, byte[] RandomizeStartParam, ref byte InfeasibleSolution, ref byte BreakOptimization, ref byte HaltOptimization, TargetFunctionCallRaw TargetFunctionRaw, int PopulationSize, int MaxGenerations, double CrossoverProbability, double MutationProbability, IStoppingCriteria StopCriteria)
	Starts a direct optimization run without using properties, these have to be given then as parameters in the funtion call, the return values are OK = 0, OPTI_ERROR_NOT_LICENSED = -1, OPTI_ERROR_TARGET_FUNCTION_NULL = -2, OPTI_ERROR_DIMENSION_OVERSIZED = -3, OPTI_ERROR_IS_RUNNING = -4, OPTI_ERROR_NO_FEASIBLE_SOLUTION = -5, OPTI_ERROR_EXCEPTION = -100 or OPTI_ERROR_ARITHMETICEXCEPTION = -101.
int	StartRaw101 (ref byte Maximization, ref int Dimension, double[] OptiPara, double[] OptiParaMin, double[] OptiParaMax, double[] OptiParaStep, byte[] OptiParaIsInvolved, byte[] OptiParaMaxIterate, double[] OptiParaOptimum, byte[] RandomizeStartParam, ref byte InfeasibleSolution, ref byte BreakOptimization, ref byte HaltOptimization, TargetFunctionCallRaw TargetFunctionRaw, int PopulationSize, int MaxGenerations, double CrossoverProbability, double MutationProbability, IStoppingCriteria StopCriteria)
	Starts a direct Stacker optimization run without using properties, these have to be given then as parameters in the funtion call, the return values are OK = 0, OPTI_ERROR_NOT_LICENSED = -1, OPTI_ERROR_TARGET_FUNCTION_NULL = -2, OPTI_ERROR_DIMENSION_OVERSIZED = -3, OPTI_ERROR_IS_RUNNING = -4, OPTI_ERROR_NO_FEASIBLE_SOLUTION = -5, OPTI_ERROR_EXCEPTION = -100 or OPTI_ERROR_ARITHMETICEXCEPTION = -101.
Public Member Functions inherited from SiMoPO.IParameterOptimization
int	Start ()
	To trigger an optimization run, the return values are OK = 0, OPTI_ERROR_NOT_LICENSED = -1, OPTI_ERROR_TARGET_FUNCTION_NULL = -2, OPTI_ERROR_DIMENSION_OVERSIZED = -3, OPTI_ERROR_IS_RUNNING = -4, OPTI_ERROR_NO_FEASIBLE_SOLUTION = -5, OPTI_ERROR_EXCEPTION = -100 or OPTI_ERROR_ARITHMETICEXCEPTION = -101.

Properties
int	PopulationSize `[get, set]`
	To set the population size, this is the number of individuals used for an optimization run.
int	MaxGenerations `[get, set]`
	To set the maximal number of generations to be used before an optimization run stops.
double	CrossoverProbability `[get, set]`
	This is the rate with which crossover between individuals occurs during propagation.
double	MutationProbability `[get, set]`
	This is rate with which mutation of an individual occurs during propagation.
double	RandomPopulationRatio `[get, set]`
	To set the ratio of random seeds with reference to the PopulationSize.
IStoppingCriteria	StoppingCriteria `[get]`
	Stopping criteria for the genetic algorithm.
string	LicenseKeyStacker `[get, set]`
	To input a Stacker Developer or Stacker Developer Deploy license string, when read the hardware or software license is stated.
Properties inherited from SiMoPO.IParameterOptimization
byte	Maximization `[get, set]`
	If TRUE the search goes for a maximum of the target function, if FALSE for a minimum.
int	Dimension `[get, set]`
	Number of parameters to be optimized.
int	Id `[get, set]`
	Id of the PO algorithm to be used with parallel computing.
double[]	OptiPara `[get]`
	Start parameters and runtime values of the parameters.
double[]	OptiParaMin `[get]`
	Minimal allowed values for the parameters.
double[]	OptiParaMax `[get]`
	Maximal allowed values for the parameters.
double[]	OptiParaStep `[get]`
	Step width for the parameters (until now only used by Simple Hill Climbing).
byte[]	OptiParaIsInvolved `[get]`
	If FALSE this parameter is not taken into optimization.
double[]	OptiParaOptimum `[get]`
	Optimal values of the parameters after an optimization run.
double	Optimum `[get]`
	Target function value after an optimization run.
byte	InfeasibleSolution `[get, set]`
	If hill climbing parameters are set to RandomizeStartParam and hill climbing property RandomPopulationSize is greater than zero the target function must set this flag if the solution is infeasible (and reset if feasible), with genetic algorithms this parameter has no meaning yet.
byte	BreakOptimization `[get, set]`
	To break an optimization run.
byte	HaltOptimization `[get, set]`
	To halt an optimization run, as a handshake the optimization sets BreakOptimization, which has to be reset by the user.
string	LicenseKey `[get, set]`
	To input a Developer or Developer Deploy license string, when read the hardware or software license is stated.
TargetFunctionCall	TargetFunction `[get, set]`
	The target funtion is the function to be searched for an optimum according the parameters.
byte	IsRunning `[get]`
	Signals the user that an optimization run is in progress.

Detailed Description

Interface to a single evolutionary and genetic algorithms thread.

Member Function Documentation

◆ StartRaw()

int SiMoPO.GA.IGeneticAlgorithms.StartRaw	(	ref byte	Maximization,
		ref int	Dimension,
		double[]	OptiPara,
		double[]	OptiParaMin,
		double[]	OptiParaMax,
		double[]	OptiParaStep,
		byte[]	OptiParaIsInvolved,
		byte[]	OptiParaMaxIterate,
		double[]	OptiParaOptimum,
		byte[]	RandomizeStartParam,
		ref byte	InfeasibleSolution,
		ref byte	BreakOptimization,
		ref byte	HaltOptimization,
		TargetFunctionCallRaw	TargetFunctionRaw,
		int	PopulationSize,
		int	MaxGenerations,
		double	CrossoverProbability,
		double	MutationProbability,
		IStoppingCriteria	StopCriteria )

Starts a direct optimization run without using properties, these have to be given then as parameters in the funtion call, the return values are OK = 0, OPTI_ERROR_NOT_LICENSED = -1, OPTI_ERROR_TARGET_FUNCTION_NULL = -2, OPTI_ERROR_DIMENSION_OVERSIZED = -3, OPTI_ERROR_IS_RUNNING = -4, OPTI_ERROR_NO_FEASIBLE_SOLUTION = -5, OPTI_ERROR_EXCEPTION = -100 or OPTI_ERROR_ARITHMETICEXCEPTION = -101.

◆ StartRaw101()

int SiMoPO.GA.IGeneticAlgorithms.StartRaw101	(	ref byte	Maximization,
		ref int	Dimension,
		double[]	OptiPara,
		double[]	OptiParaMin,
		double[]	OptiParaMax,
		double[]	OptiParaStep,
		byte[]	OptiParaIsInvolved,
		byte[]	OptiParaMaxIterate,
		double[]	OptiParaOptimum,
		byte[]	RandomizeStartParam,
		ref byte	InfeasibleSolution,
		ref byte	BreakOptimization,
		ref byte	HaltOptimization,
		TargetFunctionCallRaw	TargetFunctionRaw,
		int	PopulationSize,
		int	MaxGenerations,
		double	CrossoverProbability,
		double	MutationProbability,
		IStoppingCriteria	StopCriteria )

Starts a direct Stacker optimization run without using properties, these have to be given then as parameters in the funtion call, the return values are OK = 0, OPTI_ERROR_NOT_LICENSED = -1, OPTI_ERROR_TARGET_FUNCTION_NULL = -2, OPTI_ERROR_DIMENSION_OVERSIZED = -3, OPTI_ERROR_IS_RUNNING = -4, OPTI_ERROR_NO_FEASIBLE_SOLUTION = -5, OPTI_ERROR_EXCEPTION = -100 or OPTI_ERROR_ARITHMETICEXCEPTION = -101.

Property Documentation

◆ CrossoverProbability

double SiMoPO.GA.IGeneticAlgorithms.CrossoverProbability

getset

This is the rate with which crossover between individuals occurs during propagation.

Limits [0.0, 1.0], default value is 0.9

Referenced by StartRaw(), and StartRaw101().

◆ LicenseKeyStacker

string SiMoPO.GA.IGeneticAlgorithms.LicenseKeyStacker

getset

To input a Stacker Developer or Stacker Developer Deploy license string, when read the hardware or software license is stated.

◆ MaxGenerations

int SiMoPO.GA.IGeneticAlgorithms.MaxGenerations

getset

To set the maximal number of generations to be used before an optimization run stops.

Default value is 120

Referenced by StartRaw(), and StartRaw101().

◆ MutationProbability

double SiMoPO.GA.IGeneticAlgorithms.MutationProbability

getset

This is rate with which mutation of an individual occurs during propagation.

Limits [0.0, 1.0], default value is 0.1

Referenced by StartRaw(), and StartRaw101().

◆ PopulationSize

int SiMoPO.GA.IGeneticAlgorithms.PopulationSize

getset

To set the population size, this is the number of individuals used for an optimization run.

Default value is 100

Referenced by StartRaw(), and StartRaw101().

◆ RandomPopulationRatio

double SiMoPO.GA.IGeneticAlgorithms.RandomPopulationRatio

getset

To set the ratio of random seeds with reference to the PopulationSize.

Limits [0.0, 1.0], default value is 0.99, this means as a rule that only for one individual the OptiPara values are used as seed

◆ StoppingCriteria

IStoppingCriteria SiMoPO.GA.IGeneticAlgorithms.StoppingCriteria

get

Stopping criteria for the genetic algorithm.

Public Member Functions

Properties

Detailed Description

Member Function Documentation

◆ StartRaw()

◆ StartRaw101()

Property Documentation

◆ CrossoverProbability

◆ LicenseKeyStacker

◆ MaxGenerations

◆ MutationProbability

◆ PopulationSize

◆ RandomPopulationRatio

◆ StoppingCriteria