Modifier and Type | Field and Description |
---|---|
protected Population<T> |
GeneticAlgorithm.initialPopulation
The initial population
|
Modifier and Type | Method and Description |
---|---|
Population<T> |
GeneticAlgorithm.getCurrentPopulation()
Deprecated.
Use
GeneticAlgorithm.getLastPopulation() instead. |
Population<T> |
GeneticAlgorithm.getHistoryAt(int pos)
Returns the history population at the specified generation.
|
Population<T> |
GeneticAlgorithm.getInitialPopulation()
Returns the initial population.
|
Population<T> |
GeneticAlgorithm.getLastPopulation()
Returns the current population.
|
Population<T> |
GeneticAlgorithm.getNextPopulation()
Returns the genetic algorithm next population.
|
Modifier and Type | Method and Description |
---|---|
void |
GeneticAlgorithm.evaluatePopulation(Population<T> population)
Evaluates the population.
|
void |
GeneticAlgorithm.evaluatePopulation(Population<T> population,
boolean forced)
Evaluates the population.
|
void |
GeneticAlgorithm.evolve(Population<T> pop)
Evolves the algorithms by resetting the initial population and restarting the algorithm.
|
protected void |
GeneticAlgorithm.randomizePopulation(Population<T> pop)
Perform a population randomization, by itering on individuals.
|
Constructor and Description |
---|
GeneticAlgorithm(Fitness fitness,
Population<T> pop)
Constructs a new genetic algorithm with the specified population and the
default generation limit.
|
GeneticAlgorithm(Fitness fitness,
Population<T> pop,
int genlimit)
Constructs a new genetic algorithm with the specified population and the
specified generation limit.
|
GeneticAlgorithm(Population<T> pop)
Constructs a new genetic algorithm with the specified population and the
default generation limit.
|
GeneticAlgorithm(Population<T> pop,
int genlimit)
Constructs a new genetic algorithm with the specified population and the
specified generation limit.
|
Modifier and Type | Method and Description |
---|---|
protected void |
IslandGA.migrate(Population<T>[] branches)
Performs migration of individuals between islands
|
Constructor and Description |
---|
CrowdingGA(Fitness fitness,
Crowder crowder,
Population<T> population,
int generations)
Create a new CrowdingGA by setting the initial population and the generation limit
|
IslandGA(Fitness fitness,
Population<T> population)
Creates a IslandGA instance
|
IslandGA(Fitness fitness,
Population<T> population,
int genlimit)
Creates a IslandGA instance
|
IslandGA(Fitness fitness,
Population<T> population,
int genlimit,
int niches)
Creates a IslandGA instance
|
IslandGA(Fitness fitness,
Population<T> population,
int genlimit,
int niches,
GeneticAlgorithm<T> algo)
Creates a IslandGA instance with an empty fitness
|
IslandGA(Fitness fitness,
Population<T> population,
int genlimit,
int niches,
GeneticAlgorithm<T> algo,
int migration)
Creates a IslandGA instance with an empty fitness
|
IslandGA(Fitness fitness,
Population<T> population,
int genlimit,
int niches,
GeneticAlgorithm<T> algo,
int migration,
IslandGA.Graph geography)
Creates a IslandGA instance
|
IslandGA(Fitness fitness,
Population<T> population,
int genlimit,
int niches,
GeneticAlgorithm<T> algo,
int migration,
IslandGA.Graph geography,
IslandGA.ReplacementStrategy rp)
Creates a IslandGA instance
|
IslandGA(Fitness fitness,
Population<T> population,
int genlimit,
int niches,
int migration)
Creates a IslandGA instance
|
IslandGA(Fitness fitness,
Population<T> population,
int genlimit,
int niches,
int migration,
IslandGA.Graph geography)
Creates a IslandGA instance
|
IslandGA(Fitness fitness,
Population<T> population,
int genlimit,
int niches,
int migration,
IslandGA.Graph geography,
IslandGA.ReplacementStrategy rp)
Creates a IslandGA instance
|
NSGA2(Fitness fitness,
Population<T> population)
Generates a new NSGA2 instance
|
NSGA2(Fitness fitness,
Population<T> population,
int generations)
Generates a new NSGA2 instance
|
NSGA2(Fitness fitness,
Population<T> population,
int generations,
int trials)
Generates a new NSGA2 instance
|
SimpleGA(Fitness<T> fitness,
Population<T> population)
Builds a new SimpleGa with the default generation limit, crossover and mutation probability,
elitism, selection and crossover methods, and elitism strategy.
|
SimpleGA(Fitness<T> fitness,
Population<T> population,
int generations)
Builds a new SimpleGa with the default crossover and mutation probability,
elitism, selection and crossover methods, and elitism strategy.
|
SimpleGA(Fitness<T> fitness,
Population<T> population,
int generations,
double crossover,
double mutation)
Builds a new SimpleGa with the default
elitism, selection and crossover methods, and elitism strategy.
|
SimpleGA(Fitness<T> fitness,
Population<T> population,
int generations,
double crossover,
double mutation,
int elitism)
Builds a new SimpleGa with the default selection and crossover methods, and elitism strategy.
|
SimpleGA(Fitness<T> fitness,
Population<T> population,
int generations,
double crossover,
double mutation,
int elitism,
SimpleGA.SelectionMethod selmethod,
SimpleGA.CrossoverMethod crossmethod)
Builds a new SimpleGa with the default elitism strategy.
|
SimpleGA(Fitness<T> fitness,
Population<T> population,
int generations,
double crossover,
double mutation,
int elitism,
SimpleGA.SelectionMethod selmethod,
SimpleGA.CrossoverMethod crossmethod,
GeneticAlgorithm.ElitismStrategy es)
Builds a new SimpleGa.
|
SimpleGA(Population<T> population)
Deprecated.
|
SimpleGA(Population<T> population,
int generations)
Builds a new SimpleGa with the default crossover and mutation probability,
elitism, selection and crossover methods, and elitism strategy.
|
SimpleGA(Population<T> population,
int generations,
double crossover,
double mutation)
Deprecated.
|
SimpleGA(Population<T> population,
int generations,
double crossover,
double mutation,
int elitism)
Deprecated.
|
SimpleGA(Population<T> population,
int generations,
double crossover,
double mutation,
int elitism,
SimpleGA.SelectionMethod selmethod,
SimpleGA.CrossoverMethod crossmethod)
Deprecated.
|
SimpleGA(Population<T> population,
int generations,
double crossover,
double mutation,
int elitism,
SimpleGA.SelectionMethod selmethod,
SimpleGA.CrossoverMethod crossmethod,
GeneticAlgorithm.ElitismStrategy es)
Deprecated.
|
SteadyStateGA(Fitness fitness,
Population<T> pop)
Builds a new SteadyStateGA with default generation limit, default replacement rate,
defaul selection rate, default selection method.
|
SteadyStateGA(Fitness fitness,
Population<T> pop,
int genlimit)
Builds a new SteadyStateGA with default replacement rate,
defaul selection rate, default selection method.
|
SteadyStateGA(Fitness fitness,
Population<T> pop,
int genlimit,
int rr,
int sr,
Selector<T> selector,
AbstractStage<T>... stages)
Builds a new SteadyStateGA
|
SteadyStateGA(Fitness fitness,
Population<T> pop,
int genlimit,
int rr,
int sr,
SteadyStateGA.SelectionMethod selmethod,
AbstractStage<T>... stages)
Builds a new SteadyStateGA
|
SteadyStateGA(Fitness fitness,
Population<T> pop,
int genlimit,
Selector<T> selector,
AbstractStage<T>... stages)
Builds a new SteadyStateGA
|
SteadyStateGA(Fitness fitness,
Population<T> pop,
int genlimit,
SteadyState<T> ss)
Builds a new SteadyStateGA
|
SteadyStateGA(Fitness fitness,
Population<T> pop,
int genlimit,
SteadyStateGA.SelectionMethod selmethod,
AbstractStage<T>... stages)
Builds a new SteadyStateGA with default replacement rate,
defaul selection rate
|
Constructor and Description |
---|
RoyalGA(Population<BitwiseChromosome> pop,
int gen,
int sectionSize,
int blockSize,
int numBlocks) |
TSPGA(double[][] matrix,
Population<IntegerChromosome> pop,
int genlimit) |
Modifier and Type | Method and Description |
---|---|
Population<T> |
Individual.getPopulation()
Return the
Population that contains this Individual
|
Modifier and Type | Method and Description |
---|---|
void |
Population.add(Population<T> pop)
Adds all the individuals contained by the specified population at this
population.
|
void |
Fitness.adjust(Population<C> pop,
Individual<C>[] elite)
Adjust the evaluation after elistim is applied.
|
void |
Fitness.init(Population<C> pop)
Initializes the
Population given as parameter by resetting the
scores of all individuals. |
void |
Fitness.prepare(Population<C> pop)
Prepares the evaluation.
|
void |
Population.resizeAs(Population<T> population)
Resizes the current population.
|
void |
Population.setAs(Population<T> pop)
Sets this population as the specified one.
|
void |
Fitness.sort(Fitness.SortingMode mode,
Population<C> pop)
Sorts the
Population given as argument using the Fitness.SortingMode
given as argument
|
static <K extends Chromosome> |
Fitness.sort(Fitness.SortingMode sortingMode,
Population<K> pop,
boolean... bis)
Sorts the
Population given as argument using the Fitness.SortingMode
and the array given as arguments. |
void |
Fitness.sort(Population<C> pop)
Sorts the
Population given as argument
|
static <K extends Chromosome> |
Fitness.sort(Population<K> pop,
boolean... bis)
Sorts the
Population given as argument using the array given as
argument. |
void |
Population.swap(Population<T> pop)
Swaps this population with the speficied one; the age and the individuals will be swapped
by this operation.
|
void |
Population.Statistics.update(Population<T> population)
Updates all its information about population
|
Constructor and Description |
---|
Population(Population<T> population)
Constructs a new population from the specified one with the same size
|
Modifier and Type | Method and Description |
---|---|
abstract void |
Dispenser.distribute(Population<T> in,
Population<T>[] branches)
Distributes the specified population between those ones in the specified array.
|
abstract void |
Dispenser.distribute(Population<T> in,
Population<T>[] branches)
Distributes the specified population between those ones in the specified array.
|
void |
ExclusiveDispenser.distribute(Population<T> in,
Population<T>[] branches)
Distributes the specified population between those ones in the specified array.
|
void |
ExclusiveDispenser.distribute(Population<T> in,
Population<T>[] branches)
Distributes the specified population between those ones in the specified array.
|
protected void |
Parallel.distribute(Population<T> in,
Population<T>[] branches)
Distributes the specified population between those ones in the specified array.
|
protected void |
Parallel.distribute(Population<T> in,
Population<T>[] branches)
Distributes the specified population between those ones in the specified array.
|
abstract void |
Dispenser.mergePopulation(Population<T>[] branches,
Population<T> out)
Merges the populations within the specified array in the specified one.
|
abstract void |
Dispenser.mergePopulation(Population<T>[] branches,
Population<T> out)
Merges the populations within the specified array in the specified one.
|
void |
ExclusiveDispenser.mergePopulation(Population<T>[] branches,
Population<T> out)
Merges the populations within the specified array in the specified one.
|
void |
ExclusiveDispenser.mergePopulation(Population<T>[] branches,
Population<T> out)
Merges the populations within the specified array in the specified one.
|
protected void |
Parallel.mergePopulation(Population<T>[] branches,
Population<T> out)
Merges the populations within the specified array in the specified one.
|
protected void |
Parallel.mergePopulation(Population<T>[] branches,
Population<T> out)
Merges the populations within the specified array in the specified one.
|
void |
ExclusiveDispenser.postDistribute(Population<T>[] branches)
Callback method invoked just after distribution of individuals is done.
|
void |
ExclusiveDispenser.postMerge(Population<T> population)
Callback method invoked just after merge of individuals is done.
|
void |
ExclusiveDispenser.preDistribute(Population<T> population)
Callback method invoked just before distribution of individuals begins.
|
void |
ExclusiveDispenser.preMerge(Population<T>[] branches)
Callback method invoked just before merge of branches begins.
|
abstract void |
AbstractStage.process(Population<T> in,
Population<T> out)
Processes the input population and tranforms it into the output population.
|
abstract void |
AbstractStage.process(Population<T> in,
Population<T> out)
Processes the input population and tranforms it into the output population.
|
void |
AlgorithmStage.process(Population<T> in,
Population<T> out) |
void |
AlgorithmStage.process(Population<T> in,
Population<T> out) |
void |
BreakPoint.process(Population<T> in,
Population<T> out) |
void |
BreakPoint.process(Population<T> in,
Population<T> out) |
void |
Evaluator.process(Population<T> in,
Population<T> out)
Performs an evaluation of input population.
|
void |
Evaluator.process(Population<T> in,
Population<T> out)
Performs an evaluation of input population.
|
void |
Parallel.process(Population<T> in,
Population<T> out) |
void |
Parallel.process(Population<T> in,
Population<T> out) |
void |
Sequence.process(Population<T> in,
Population<T> out)
Invokes the process method on all of its internal stages
|
void |
Sequence.process(Population<T> in,
Population<T> out)
Invokes the process method on all of its internal stages
|
Modifier and Type | Method and Description |
---|---|
protected void |
Selector.preSelect(Population<T> pop,
Population.Filter filter)
Sets up the selection state according a population's state.
|
protected abstract void |
Crowder.preselect(Population<T> in,
Population<T> out)
Preselection of individuals before processing.
|
protected abstract void |
Crowder.preselect(Population<T> in,
Population<T> out)
Preselection of individuals before processing.
|
void |
Crossover.process(Population<T> in,
Population<T> out) |
void |
Crossover.process(Population<T> in,
Population<T> out) |
void |
Crowder.process(Population<T> in,
Population<T> out)
Performs crowding processing, according the the following scheme:
pre = preselect(in)
evo = body.process(pre)
out = replace(evo)
|
void |
Crowder.process(Population<T> in,
Population<T> out)
Performs crowding processing, according the the following scheme:
pre = preselect(in)
evo = body.process(pre)
out = replace(evo)
|
void |
Mutator.process(Population<T> in,
Population<T> out) |
void |
Mutator.process(Population<T> in,
Population<T> out) |
void |
Scaling.process(Population<T> in,
Population<T> out) |
void |
Scaling.process(Population<T> in,
Population<T> out) |
void |
Selector.process(Population<T> in,
Population<T> out)
Sets the individuals in the output population like the selected ones
|
void |
Selector.process(Population<T> in,
Population<T> out)
Sets the individuals in the output population like the selected ones
|
protected abstract void |
Crowder.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out)
Implements the replacement policy.
|
protected abstract void |
Crowder.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out)
Implements the replacement policy.
|
protected abstract void |
Crowder.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out)
Implements the replacement policy.
|
protected abstract void |
Crowder.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out)
Implements the replacement policy.
|
abstract void |
Scaling.scale(Population<T> pop)
Method used to scale the fitness of indivduals belonging to given
Population
|
Individual<T> |
Selector.select(Population<T> pop)
Selects an individual in the population
|
Modifier and Type | Method and Description |
---|---|
protected void |
RouletteWheelSelector.preSelect(Population<T> pop,
Population.Filter filter) |
protected void |
DeJongCrowder.preselect(Population<T> in,
Population<T> out) |
protected void |
DeJongCrowder.preselect(Population<T> in,
Population<T> out) |
protected void |
DeterministicCrowder.preselect(Population<T> in,
Population<T> out) |
protected void |
DeterministicCrowder.preselect(Population<T> in,
Population<T> out) |
protected void |
MultiNicheCrowder.preselect(Population<T> in,
Population<T> out) |
protected void |
MultiNicheCrowder.preselect(Population<T> in,
Population<T> out) |
protected void |
SteadyState.preselect(Population<T> in,
Population<T> out) |
protected void |
SteadyState.preselect(Population<T> in,
Population<T> out) |
protected void |
DeJongCrowder.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out) |
protected void |
DeJongCrowder.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out) |
protected void |
DeJongCrowder.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out) |
protected void |
DeJongCrowder.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out) |
protected void |
DeterministicCrowder.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out) |
protected void |
DeterministicCrowder.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out) |
protected void |
DeterministicCrowder.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out) |
protected void |
DeterministicCrowder.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out) |
protected void |
MultiNicheCrowder.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out) |
protected void |
MultiNicheCrowder.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out) |
protected void |
MultiNicheCrowder.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out) |
protected void |
MultiNicheCrowder.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out) |
protected void |
SteadyState.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out) |
protected void |
SteadyState.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out) |
protected void |
SteadyState.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out) |
protected void |
SteadyState.replace(Population<T> initial,
Population<T> preselected,
Population<T> evolved,
Population<T> out) |
void |
ProportionalScaling.scale(Population<T> pop) |
void |
RankScaling.scale(Population<T> pop) |
void |
TopScaling.scale(Population<T> pop) |
Modifier and Type | Method and Description |
---|---|
void |
SimpleDispenser.preDistribute(Population<T> population) |
Constructor and Description |
---|
PatternGA(Population<IntegerChromosome> pop,
int numGen) |
Constructor and Description |
---|
TSPGA(double[][] matrix,
Population<IntegerChromosome> pop,
int genlimit) |
Modifier and Type | Method and Description |
---|---|
void |
SimpleDispenser.preDistribute(Population<T> population) |
Constructor and Description |
---|
PatternGA(Population<IntegerChromosome> pop,
int numGen) |
Constructor and Description |
---|
TSPGA(double[][] matrix,
Population<IntegerChromosome> pop,
int genlimit) |
Modifier and Type | Method and Description |
---|---|
void |
Runner.execute(GeneticAlgorithm algorithm,
Population<?> initialPopulation)
Start evolving the algorithm given and adopting as initial population the
one passed as argument
|
void |
MultiThreadEvaluator.onEvaluationBegin(Population pop,
boolean forced) |
void |
Runner.onEvaluationBegin(Population pop,
boolean forced)
Call-back invoked soon before the
Population evaluation starts
using the default Fitness defined per GeneticAlgorithm |