Modifier and Type | Method and Description |
---|---|
void |
AlgorithmEventListener.onAlgorithmInit(GeneticAlgorithm<T> ga,
long time)
Invoked when the genetic algorithm is initialized
|
void |
AlgorithmEventListener.onAlgorithmStart(GeneticAlgorithm<T> ga,
long time)
Invoked when the genetic algorithm starts
|
void |
AlgorithmEventListener.onAlgorithmStop(GeneticAlgorithm<T> ga,
long time)
Invoked when the genetic algorithm ends
|
void |
GenerationEventListener.onGeneration(GeneticAlgorithm<T> ga,
long time)
Invoked when at the end of one generation step
|
Modifier and Type | Class and Description |
---|---|
class |
CrowdingGA<T extends Chromosome>
A genetic algorithm based on crowding
|
class |
IslandGA<T extends Chromosome>
IslandGA implements a niche based algorithm
|
class |
NSGA2<T extends Chromosome>
Multi-objective genetic algorithm as proposed by Deb.
|
class |
SimpleGA<T extends Chromosome>
A facade providing a simple interface to GeneticAlgorithm.
|
class |
SteadyStateGA<T extends Chromosome>
Steady-state genetic algorithm
|
Modifier and Type | Method and Description |
---|---|
GeneticAlgorithm<T>[] |
IslandGA.getAllIslands()
Gets the list of algorithms used for the different islands
|
Modifier and Type | Method and Description |
---|---|
void |
IslandGA.setAllIslands(GeneticAlgorithm<T> algo)
Sets the algorithm for all islands
|
void |
IslandGA.setIsland(int i,
GeneticAlgorithm<T> algo)
Sets the algorithm for a specific island
|
Constructor and Description |
---|
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
|
Modifier and Type | Class and Description |
---|---|
class |
RoyalGA |
class |
TSPGA |
Modifier and Type | Field and Description |
---|---|
protected GeneticAlgorithm<BitwiseChromosome> |
DeJongTest.ga |
protected GeneticAlgorithm<BitwiseChromosome> |
RoyalTest.ga |
Modifier and Type | Field and Description |
---|---|
protected GeneticAlgorithm<T> |
AbstractStage.ga
The genetic algorithm, this stage belongs to
|
Modifier and Type | Method and Description |
---|---|
GeneticAlgorithm<T> |
AlgorithmStage.getAlgorithm()
Returns the wrapped algorithm
|
Modifier and Type | Method and Description |
---|---|
void |
AbstractStage.init(GeneticAlgorithm<T> ga)
Initializes this stage according to the genetic algorithm
that uses it
|
void |
Parallel.init(GeneticAlgorithm<T> ga) |
void |
Sequence.init(GeneticAlgorithm<T> ga)
Initializes all of its internal stages
|
Constructor and Description |
---|
AlgorithmStage(GeneticAlgorithm<T> algorithm)
Builds a wrapper stage for the specified algorithm.
|
Modifier and Type | Method and Description |
---|---|
void |
Crossover.init(GeneticAlgorithm<T> ga) |
void |
Crowder.init(GeneticAlgorithm<T> ga) |
void |
Selector.init(GeneticAlgorithm<T> ga) |
Modifier and Type | Class and Description |
---|---|
class |
PatternGA
Tutorial showing how to extend
GeneticAlgorithm and how to use
the flexible and configurable breeding structure in Jenes. |
Modifier and Type | Method and Description |
---|---|
void |
PatternProblem.onGeneration(GeneticAlgorithm ga,
long time) |
Modifier and Type | Class and Description |
---|---|
class |
KnapsackGA
Tutorial showing how to minimization and maximization sub-prolems can cohesists in
the breeding structure of Jenes.
|
Modifier and Type | Method and Description |
---|---|
void |
PatternProblem.onGeneration(GeneticAlgorithm ga,
long time) |
Modifier and Type | Method and Description |
---|---|
static GeneticAlgorithm<DoubleChromosome> |
NumericCrossover.buildGA(Crossover<DoubleChromosome> crossover,
int n) |
Modifier and Type | Field and Description |
---|---|
protected GeneticAlgorithm |
Runner.algorithm
The algorithm to evolve in the enviroinment
|
Modifier and Type | Method and Description |
---|---|
GeneticAlgorithm |
Runner.getGeneticAlgorithm()
Return the Genetic Algorithm currently in execution in this enviroinment
|
Modifier and Type | Method and Description |
---|---|
void |
Runner.execute(GeneticAlgorithm algoritm)
Start evolving the algorithm given as argument restarting its state
|
void |
Runner.execute(GeneticAlgorithm algorithm,
boolean restart)
Start evolving the algorithm given as parameter in this enviroinment by
applying to specificated restart flag.
|
void |
Runner.execute(GeneticAlgorithm algorithm,
Population<?> initialPopulation)
Start evolving the algorithm given and adopting as initial population the
one passed as argument
|
void |
Runner.setAlgorithm(GeneticAlgorithm algorithm)
Set the Genetic Algorithm to the runner.
|