Package org.moeaframework.algorithm

Class PAES

java.lang.Object

org.moeaframework.algorithm.AbstractAlgorithm

org.moeaframework.algorithm.AbstractEvolutionaryAlgorithm

org.moeaframework.algorithm.PAES

All Implemented Interfaces:

Algorithm, Configurable, EvolutionaryAlgorithm, Stateful

public class PAES
extends AbstractEvolutionaryAlgorithm

Implementation of the (1+1) Pareto Archived Evolution Strategy (PAES). PAES uses an adaptive grid archive to maintain a diverse set of solutions.

References:

1. Knowles, J.D. and D. W. Corne (1999). The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Pareto Multiobjective Optimisation. In Proceedings of the 1999 Congress on Evolutionary Computation (CEC'99), pp. 98-105.

Field Summary

Fields inherited from class org.moeaframework.algorithm.AbstractAlgorithm

initialized, numberOfEvaluations, problem, terminated

Constructor Summary

Constructors

Constructor	Description
PAES(Problem problem)	Constructs a new PAES instance with default settings.
PAES(Problem problem, Mutation mutation, int bisections, int archiveSize)	Constructs a new PAES instance.

Method Summary

Modifier and Type	Method	Description
void	applyConfiguration(TypedProperties properties)	Applies the properties to this instance.
AdaptiveGridArchive	getArchive()	Returns the current non-dominated archive of the best solutions generated by this evolutionary algorithm, or null if no archive is used.
TypedProperties	getConfiguration()	Gets the current configuration of this instance.
Mutation	getVariation()	Returns the variation operator currently in use by this algorithm.
protected void	iterate()	Performs one iteration of the algorithm.
void	setVariation(Mutation mutation)	Replaces the mutation operator to be used by this algorithm.
Solution	test(Solution parent, Solution offspring)	The test procedure to determine which solution, the parent or offspring, moves on to the next generation.

Methods inherited from class org.moeaframework.algorithm.AbstractEvolutionaryAlgorithm

getInitialization, getInitialPopulationSize, getPopulation, getResult, initialize, loadState, saveState, setArchive, setInitialization, setInitialPopulationSize, setPopulation, setVariation

Methods inherited from class org.moeaframework.algorithm.AbstractAlgorithm

assertNotInitialized, evaluate, evaluateAll, evaluateAll, getNumberOfEvaluations, getProblem, isInitialized, isTerminated, 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

Constructor Details

PAES

public PAES(Problem problem)

Constructs a new PAES instance with default settings.

Parameters:

problem - the problem

PAES

public PAES(Problem problem,
 Mutation mutation,
 int bisections,
 int archiveSize)

Constructs a new PAES instance.

Parameters:

problem - the problem

mutation - the mutation operator

bisections - the number of bisections in the adaptive grid archive

archiveSize - the capacity of the adaptive grid archive

Method Details

setVariation

public void setVariation(Mutation mutation)

Replaces the mutation operator to be used by this algorithm.

Parameters:

mutation - the mutation operator

getVariation

public Mutation getVariation()

Description copied from class: AbstractEvolutionaryAlgorithm

Returns the variation operator currently in use by this algorithm.

Overrides:

getVariation in class AbstractEvolutionaryAlgorithm

Returns:

the variation operator

getArchive

public AdaptiveGridArchive getArchive()

Description copied from interface: EvolutionaryAlgorithm

Returns the current non-dominated archive of the best solutions generated by this evolutionary algorithm, or null if no archive is used.

Specified by:

getArchive in interface EvolutionaryAlgorithm

Overrides:

getArchive in class AbstractEvolutionaryAlgorithm

Returns:

the current non-dominated archive of the best solutions generated by this evolutionary algorithm, or null if no archive is used

test

public Solution test(Solution parent,
 Solution offspring)

The test procedure to determine which solution, the parent or offspring, moves on to the next generation. The solution in a lower density region in the archive is returned.

Parameters:

parent - the parent solution

offspring - the offspring solution

Returns:

the solution moving on to the next genreation

iterate

protected void iterate()

Description copied from class: AbstractAlgorithm

Performs one iteration of the algorithm. This method should be overridden by implementations to perform each logical iteration of the algorithm.

Specified by:

iterate in class AbstractAlgorithm

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