Inheritance diagram for es.us.isa.generator.FM.Evolutionay.EvolutionaryFMGenerator:

Public Member Functions
	EvolutionaryFMGenerator (float crossoverProb, float mutationProb, int populationSize, int extraChromosomes, int maxIterations, int maxGenerations, double fitnessToBeat, int improvementFactor, AbstractFMGenerator generator, boolean isRolueteSelection, int opponentsPerTournament) throws BettyException
	EvolutionaryFMGenerator () throws BettyException
VariabilityModel	generateFM (Characteristics ch) throws BettyException
void	mutation (Chromosome c, GeneratorCharacteristics chars)
void	mutantOperator1 (List< String[]> tree, int position)
void	mutantOperator2 (List< String[]> tree, int position, int maxChildren)
void	mutantOperator3 (List< String[]> ctc, int position)
void	mutantOperator4 (List< String[]> ctc, int position, int numberOfFeatures)
String	printPopulation ()
Chromosome	tournamentSelection (int numberOfOpponents)
void	crossover1P (Chromosome mum, Chromosome dad, List< Chromosome > population)
double	evaluate (Chromosome chromosome)
void	setGenerator (AbstractFMGenerator generator) throws BettyException
void	setFitnessFunction (FitnessFunction ff) throws BettyException
void	setCrossoverProb (float crossoverProb) throws BettyException
void	setMutationProb (float mutationProb) throws BettyException
void	setPopulationSize (int populationSize) throws BettyException
void	setExtraChromosomes (int extraChromosomes) throws BettyException
void	setMaxIterations (int maxIterations) throws BettyException
void	setMaxGenerations (int maxGenerations) throws BettyException
void	setfitnessToBeat (double fitnessToBeat)
void	setImprovementFactor (int improvementFactor)
void	setRolueteSelection (boolean isRolueteSelection)
void	setOpponentsPerTournament (int opponentsPerTournament) throws BettyException
void	setMaximize (boolean max)
int	getBestFitnessGeneration ()
void	setBestFitnessGeneration (int bestFitnessGeneration)
double	getBestFitness ()
void	setBestFitness (double bestFitness)
List< Double >	getAvFitnesses ()
void	setAvFitnesses (List< Double > avFitnesses)
Chromosome	getChr ()
void	setChr (Chromosome chr)
Public Attributes
SortedMap< Double, List < Chromosome > >	population
Package Attributes
double	fitnesses
int	individuals
List< Double >	AvFitnesses
FitnessFunction	ff
Chromosome	chr

Detailed Description

A novel evolutionary algorithm for solving optimization problems on feature models. The algorithm takes several size parameters and an objective function as input and returns a feature model of the given size maximizing or minimizing the optimization criteria defined by the function.

Constructor & Destructor Documentation

es.us.isa.generator.FM.Evolutionay.EvolutionaryFMGenerator.EvolutionaryFMGenerator ( ) throws BettyException

Constructor using defaults parameters

Exceptions:

BettyException

Member Function Documentation

void es.us.isa.generator.FM.Evolutionay.EvolutionaryFMGenerator.crossover1P	(	Chromosome	mum,
		Chromosome	dad,
		List< Chromosome >	population
	)

These are the techniques used to combine chromosomes in some way and produce new individuals in an analogous way to biological reproduction. Single point crossover

TREE CROSSOVER

VariabilityModel es.us.isa.generator.FM.Evolutionay.EvolutionaryFMGenerator.generateFM ( Characteristics ch ) throws BettyException

Generate a variability model with the characteristic specified as input.

Parameters:

ch	The preferences of the user for the generation.

Returns:: a variability model.

Exceptions:

BettyException

Number of iterations without improvement

Variable to sum the fitnesses and then save the average on each generation

Reset statistics

Implements es.us.isa.generator.IGenerator.

void es.us.isa.generator.FM.Evolutionay.EvolutionaryFMGenerator.mutantOperator1	(	List< String[]>	tree,
		int	position
	)

Operator 1. It changes randomly the type of a relationship in the tree array, e.g. from mandatory, [M, 3] , to optional,[ O, 3 ].

Parameters:

tree	The tree containing the relationship
position	The position to do the change

void es.us.isa.generator.FM.Evolutionay.EvolutionaryFMGenerator.mutantOperator2	(	List< String[]>	tree,
		int	position,
		int	maxChildren
	)

It changes randomly the number of children of a feature in the tree, e.g. from [ M, 3 ] to [ M, 5 ]. The new number of children is the range [0, BF ] where BF is the maximum branching factor indicated as input.

The tree containing the children The position to do the change

Parameters:

maxChildren The new max number of Children

void es.us.isa.generator.FM.Evolutionay.EvolutionaryFMGenerator.mutantOperator3	(	List< String[]>	ctc,
		int	position
	)

Operator 3. It changes the type of a cross-tree constraint in the CTC array, e.g. from excludes [ E, 3, 6 ] to requires [ R, 3, 6 ].

Parameters:

ctc	The ctc to be changed
position	The position where it is

void es.us.isa.generator.FM.Evolutionay.EvolutionaryFMGenerator.mutantOperator4	(	List< String[]>	ctc,
		int	position,
		int	numberOfFeatures
	)

Operator 4. It changes randomly (with equal probability) the origin or destination feature of a constraint in the CTC array, e.g. from [ E, 3, 6 ] to requires [ E, 1, 6 ]. Origin and destination features are ensured to be different.

Parameters:

ctc	The ctc to be changed
position	The position to do the change
numberOfFeatures	the number of features of the model

void es.us.isa.generator.FM.Evolutionay.EvolutionaryFMGenerator.mutation	(	Chromosome	c,
		GeneratorCharacteristics	chars
	)

In this methods, random changes are applied to the chromosomes to prevent the algorithm from getting stuck prematurely at a locally optimal solution. Mutation operators must be specifically designed for the type of encoding used.

Parameters:

c	The chromosome
chars	The characteristics of the generator

String es.us.isa.generator.FM.Evolutionay.EvolutionaryFMGenerator.printPopulation ( )

Print population in a readable format

Returns:: The String in a readable format

Chromosome es.us.isa.generator.FM.Evolutionay.EvolutionaryFMGenerator.tournamentSelection ( int numberOfOpponents )

Determines how the individuals of one generation are selected to be combined and produce new offspring. Tournament Selection

Parameters:

numberOfOpponents The number of opponents used in the tournament

Returns:: A chromosome with updated info

Select the index of the chosen chromosme: As we use a sorted Map an it is a maximization problem we chose the solution with a smaller index.

Get the chosen chromosme from population map:

If the chosen chromosome is not in this list we go to to next list.

The chosen chormosome is in this list, so we get it:

Member Data Documentation

List<Double> es.us.isa.generator.FM.Evolutionay.EvolutionaryFMGenerator.AvFitnesses [package]

Save the average fitness of each generation

double es.us.isa.generator.FM.Evolutionay.EvolutionaryFMGenerator.fitnesses [package]

Variable to sum the fitnesses and then save the average on each generation

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

/home/malawito/Documentos/workspaces/BeTTy/Betty1.0/src/es/us/isa/generator/FM/Evolutionay/EvolutionaryFMGenerator.java

Public Member Functions

Public Attributes

Package Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation