Package org.moeaframework.algorithm.single

Class SingleObjectiveEvolutionaryAlgorithm

java.lang.Object

org.moeaframework.algorithm.AbstractAlgorithm

org.moeaframework.algorithm.AbstractEvolutionaryAlgorithm

org.moeaframework.algorithm.single.SingleObjectiveEvolutionaryAlgorithm

All Implemented Interfaces:

Algorithm, Configurable, EvolutionaryAlgorithm, Stateful

Direct Known Subclasses:

DifferentialEvolution, EvolutionStrategy, GeneticAlgorithm

public abstract class SingleObjectiveEvolutionaryAlgorithm
extends AbstractEvolutionaryAlgorithm

Abstract class for building single-objective evolutionary algorithms. These use an aggregating or scalarizing function that converts multiple objective values into a single fitness value.

Field Summary

Fields

Modifier and Type	Field	Description
protected AggregateObjectiveComparator	comparator	The aggregate objective comparator.

Fields inherited from class org.moeaframework.algorithm.AbstractAlgorithm

initialized, numberOfEvaluations, problem, terminated

Constructor Summary

Constructors

Constructor	Description
SingleObjectiveEvolutionaryAlgorithm(Problem problem, int initialPopulationSize, Population population, NondominatedPopulation archive, AggregateObjectiveComparator comparator, Initialization initialization, Variation variation)	Constructs a new single-objective algorithm.

Method Summary

Modifier and Type	Method	Description
void	applyConfiguration(TypedProperties properties)	Applies the properties to this instance.
AggregateObjectiveComparator	getComparator()	Returns the aggregate objective comparator that scalarizes multiple objectives into a single fitness value.
TypedProperties	getConfiguration()	Gets the current configuration of this instance.
NondominatedPopulation	getResult()	Returns the current best-known result.
void	setComparator(AggregateObjectiveComparator comparator)	Sets the aggregate objective comparator that scalarizes multiple objectives into a single fitness value.
void	setInitialPopulationSize(int initialPopulationSize)	Sets the initial population size.

Methods inherited from class org.moeaframework.algorithm.AbstractEvolutionaryAlgorithm

getArchive, getInitialization, getInitialPopulationSize, getPopulation, getVariation, initialize, loadState, saveState, setArchive, setInitialization, setPopulation, setVariation

Methods inherited from class org.moeaframework.algorithm.AbstractAlgorithm

assertNotInitialized, evaluate, evaluateAll, evaluateAll, getNumberOfEvaluations, getProblem, isInitialized, isTerminated, iterate, step, terminate

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.moeaframework.core.Algorithm

evaluate, getNumberOfEvaluations, getProblem, isTerminated, run, run, step, terminate

Field Details

comparator

protected AggregateObjectiveComparator comparator

The aggregate objective comparator.

Constructor Details

SingleObjectiveEvolutionaryAlgorithm

public SingleObjectiveEvolutionaryAlgorithm(Problem problem,
 int initialPopulationSize,
 Population population,
 NondominatedPopulation archive,
 AggregateObjectiveComparator comparator,
 Initialization initialization,
 Variation variation)

Constructs a new single-objective algorithm.

Parameters:

problem - the problem to solve

initialPopulationSize - the initial population size

population - the population

archive - the archive storing the non-dominated solutions

comparator - the aggregate objective comparator

initialization - the initialization method

variation - the variation operator

Method Details

getResult

public NondominatedPopulation getResult()

Description copied from interface: Algorithm

Returns the current best-known result.

Specified by:

getResult in interface Algorithm

Overrides:

getResult in class AbstractEvolutionaryAlgorithm

Returns:

the current best-known result

getComparator

public AggregateObjectiveComparator getComparator()

Returns the aggregate objective comparator that scalarizes multiple objectives into a single fitness value.

Returns:

the aggregate objective comparator

setComparator

public void setComparator(AggregateObjectiveComparator comparator)

Sets the aggregate objective comparator that scalarizes multiple objectives into a single fitness value.

Parameters:

comparator - the aggregate objective comparator

setInitialPopulationSize

public void setInitialPopulationSize(int initialPopulationSize)

Description copied from class: AbstractEvolutionaryAlgorithm

Sets the initial population size. This value can not be set after initialization.

Overrides:

setInitialPopulationSize in class AbstractEvolutionaryAlgorithm

Parameters:

initialPopulationSize - the initial population size

applyConfiguration

public void applyConfiguration(TypedProperties properties)

Description copied from interface: Configurable

Applies the properties to this instance. It is strongly recommended to apply a configuration immediately after creating the instance, as some properties can not be changed after the class is used. Exceptions may be thrown if attempting to set such properties. After calling this method, we encourage users to call TypedProperties.warnIfUnaccessedProperties() to verify all properties were processed. This can identify simple mistakes like typos. If overriding this method, properties should only be updated if a new value is provided. Additionally, if updating any Configurable objects inside this object, they should be updated before calling super.applyConfiguration(properties).

Parameters:

properties - the user-defined properties

getConfiguration

public TypedProperties getConfiguration()

Description copied from interface: Configurable

Gets the current configuration of this instance. In theory, these properties should be able to create a duplicate instance. Note however, they are unlikely to behave identically due to random numbers and other transient fields.

Returns:

the properties defining this instance