Package org.moeaframework.algorithm

Class VEGA

java.lang.Object

org.moeaframework.algorithm.AbstractAlgorithm

org.moeaframework.algorithm.AbstractEvolutionaryAlgorithm

org.moeaframework.algorithm.VEGA

All Implemented Interfaces:

Algorithm, Configurable, EvolutionaryAlgorithm, Stateful

public class VEGA
extends AbstractEvolutionaryAlgorithm

Implementation of the Vector Evaluated Genetic Algorithm (VEGA). VEGA should be avoided in practice, since many modern algorithms outperform it and exhibit better convergence properties, but is included due to its historical significance. VEGA is considered the earliest MOEA. It supports M objectives during the selection phase by selecting M different subgroups, each selected based on the i-th objective value, for i=1,...,M.

There is one small algorithmic difference between this implementation and [1]. In [1], applying the genetic operators fills the entire population. However, since custom variation operators can be specified, it is possible that the population will not be filled completely. As a result, this implementation will continue selecting parents until the population is full.

References:

1. Schaffer, D. (1985). Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. Proceedings of the 1st International Conference on Genetic Algorithms, pp. 93-100.

Field Summary

Fields inherited from class org.moeaframework.algorithm.AbstractAlgorithm

initialized, numberOfEvaluations, problem, terminated

Constructor Summary

Constructors

Constructor	Description
VEGA(Problem problem)	Constructs a new VEGA instance with default settings.
VEGA(Problem problem, int initialPopulationSize, Population population, NondominatedPopulation archive, Initialization initialization, Variation variation)	Constructs a new VEGA instance.

Method Summary

Modifier and Type	Method	Description
protected void	iterate()	Performs one iteration of the algorithm.
void	setInitialPopulationSize(int initialPopulationSize)	Sets the initial population size.
void	setVariation(Variation variation)	Replaces the variation operator to be used by this algorithm.

Methods inherited from class org.moeaframework.algorithm.AbstractEvolutionaryAlgorithm

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

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

Methods inherited from interface org.moeaframework.core.configuration.Configurable

applyConfiguration, getConfiguration

Constructor Details

VEGA

public VEGA(Problem problem)

Constructs a new VEGA instance with default settings.

Parameters:

problem - the problem

VEGA

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

Constructs a new VEGA instance.

Parameters:

problem - the problem

initialPopulationSize - the initial population size

population - the population

archive - the external archive; or null if no external archive is used

initialization - the initialization operator

variation - the variation operator

Method Details

setVariation

public void setVariation(Variation variation)

Description copied from class: AbstractEvolutionaryAlgorithm

Replaces the variation operator to be used by this algorithm.

Overrides:

setVariation in class AbstractEvolutionaryAlgorithm

Parameters:

variation - the variation operator

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

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