net.sourceforge.cilib.coevolution
Class CoevolutionAlgorithm

java.lang.Object
  net.sourceforge.cilib.algorithm.Algorithm
      net.sourceforge.cilib.algorithm.population.PopulationBasedAlgorithm
          net.sourceforge.cilib.algorithm.population.MultiPopulationBasedAlgorithm
              net.sourceforge.cilib.coevolution.CoevolutionAlgorithm

All Implemented Interfaces:

Serializable, Iterable<PopulationBasedAlgorithm>, Runnable, Cloneable

public class CoevolutionAlgorithm
extends MultiPopulationBasedAlgorithm

This is the framework for a coevolution algorithm, either competitive or cooperative

Author:

Julien Duhain, leo TODO: impliment and test cooperative side TODO: This code needs a lot of love, need to refactor!

See Also:

Serialized Form

Field Summary

protected CoevolutionIterationStrategy

coevolutionIterationStrategy

Fields inherited from class net.sourceforge.cilib.algorithm.population.MultiPopulationBasedAlgorithm

algorithmIterator, subPopulationsAlgorithms

Fields inherited from class net.sourceforge.cilib.algorithm.Algorithm

optimisationProblem

Constructor Summary

CoevolutionAlgorithm()

CoevolutionAlgorithm(CoevolutionAlgorithm copy)

Method Summary

void	algorithmIteration() The actual operations that the current Algorithm performs within a single iteration.
DomainRegistry	getBehaviouralDomain()
OptimisationSolution	getBestSolution() This doesnt really make sense since in co-evolution you have a best solution for each population
CoevolutionAlgorithm	getClone() Create a cloned copy of the current object and return it.
CoevolutionIterationStrategy	getCoevolutionIterationStrategy()
CoevolutionOptimisationProblem	getCoevolutionOptimisationProblem()
DataSetBuilder	getDataSetBuilder()
double	getDiameter()
DomainRegistry	getDomain()
int	getFitnessEvaluations()
int	getPopulationSize()
double	getRadius()
List<OptimisationSolution>	getSolutions() Can be useful to compare how the different populations are performing.
Topology<? extends Entity>	getTopology() Get the current collection (population) of entities.
void	performInitialisation() initialises every population.
void	setAlgorithm(PopulationBasedAlgorithm algorithm) Add a subpopulation Algorithm
void	setCoevolutionIterationStrategy(CoevolutionIterationStrategy coevolutionIterationStrategy)
void	setDataSetBuilder(DataSetBuilder dataSet)
void	setOptimisationProblem(OptimisationProblem problem) Set the optimisation problem to be solved.
void	setPopulationSize(int i)
void	setTopology(Topology t)

Methods inherited from class net.sourceforge.cilib.algorithm.population.MultiPopulationBasedAlgorithm

accept, addPopulationBasedAlgorithm, getAlgorithmIterator, getInitialisationStrategy, getPopulations, iterator, removePopulationBasedalgorithm, reset, setAlgorithmIterator, setInitialisationStrategy, setPopulations

Methods inherited from class net.sourceforge.cilib.algorithm.Algorithm

addAlgorithmListener, addStoppingCondition, get, getAlgorithmList, getIterations, getOptimisationProblem, getPercentageComplete, getStoppingConditions, initialise, isFinished, performIteration, performUninitialisation, removeAlgorithmListener, removeStoppingCondition, run, terminate

Methods inherited from class java.lang.Object

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

Field Detail

coevolutionIterationStrategy

protected CoevolutionIterationStrategy coevolutionIterationStrategy

Constructor Detail

CoevolutionAlgorithm

public CoevolutionAlgorithm()

CoevolutionAlgorithm

public CoevolutionAlgorithm(CoevolutionAlgorithm copy)

Method Detail

getClone

public CoevolutionAlgorithm getClone()

Create a cloned copy of the current object and return it. In general the created copy will be a deep copy of the provided instance. As a result this operation an be quite expensive if used incorrectly.

Specified by:

getClone in interface Cloneable

Specified by:

getClone in class PopulationBasedAlgorithm

Returns:

An exact clone of the current object instance.

See Also:

Object.clone()

getPopulationSize

public int getPopulationSize()

Returns:

the sum of all the populations

setAlgorithm

public void setAlgorithm(PopulationBasedAlgorithm algorithm)

Add a subpopulation Algorithm

Parameters:

algorithm -

getCoevolutionOptimisationProblem

public CoevolutionOptimisationProblem getCoevolutionOptimisationProblem()

Returns:

optimization problem cast to type CoevolutionOptimizationProblem

performInitialisation

public void performInitialisation()

initialises every population.

Overrides:

performInitialisation in class Algorithm

getBestSolution

public OptimisationSolution getBestSolution()

This doesnt really make sense since in co-evolution you have a best solution for each population

Specified by:

getBestSolution in class PopulationBasedAlgorithm

Returns:

The OptimisationSolution representing the best solution.

getSolutions

public List<OptimisationSolution> getSolutions()

Can be useful to compare how the different populations are performing.

Specified by:

getSolutions in class PopulationBasedAlgorithm

Returns:

a list of the best solution in each population.

algorithmIteration

public void algorithmIteration()

The actual operations that the current Algorithm performs within a single iteration.

Specified by:

algorithmIteration in class MultiPopulationBasedAlgorithm

getCoevolutionIterationStrategy

public CoevolutionIterationStrategy getCoevolutionIterationStrategy()

setCoevolutionIterationStrategy

public void setCoevolutionIterationStrategy(CoevolutionIterationStrategy coevolutionIterationStrategy)

getFitnessEvaluations

public int getFitnessEvaluations()

getDomain

public DomainRegistry getDomain()

getBehaviouralDomain

public DomainRegistry getBehaviouralDomain()

getDataSetBuilder

public DataSetBuilder getDataSetBuilder()

setDataSetBuilder

public void setDataSetBuilder(DataSetBuilder dataSet)

setPopulationSize

public void setPopulationSize(int i)

getRadius

public double getRadius()

getDiameter

public double getDiameter()

setTopology

public void setTopology(Topology t)

getTopology

public Topology<? extends Entity> getTopology()

Description copied from class: PopulationBasedAlgorithm

Get the current collection (population) of entities.

Overrides:

getTopology in class MultiPopulationBasedAlgorithm

Returns:

The current population.

setOptimisationProblem

public void setOptimisationProblem(OptimisationProblem problem)

Set the optimisation problem to be solved. By default, the problem is null. That is, it is necessary to set the optimisation problem before calling Algorithm.initialise().

Overrides:

setOptimisationProblem in class Algorithm

Parameters:

problem - An implementation of the OptimisationProblemAdapter interface.