net.sourceforge.cilib.algorithm.population
Interface IterationStrategy<E extends PopulationBasedAlgorithm>

Type Parameters:

E - The PopulationBasedAlgorithm type.

All Superinterfaces:

Cloneable, Serializable

All Known Implementing Classes:

AbstractIterationStrategy, ArchivingIterationStrategy, ASynchronousIterationStrategy, CoevolutionIterationStrategy, CoevolutionSynchronousIterationStrategy, CompetitiveCoevolutionIterationStrategy, DifferentialEvolutionIterationStrategy, DynamicIterationStrategy, GeneticAlgorithmIterationStrategy, SynchronousIterationStrategy

public interface IterationStrategy<E extends PopulationBasedAlgorithm>
extends Cloneable, Serializable

Interface to define the manner in which an iteration is defined for a PopulationBasedAlgorithm.

Method Summary

IterationStrategy<E>	getClone() Create a cloned copy of the current object and return it.
void	performIteration(E algorithm) Perform the iteration of the PopulationBasedAlgorithm.

Method Detail

getClone

IterationStrategy<E> 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

Returns:

An exact clone of the current object instance.

See Also:

Object.clone()

performIteration

void performIteration(E algorithm)

Perform the iteration of the PopulationBasedAlgorithm.

Due to the nature of the PopulationBasedAlgorithms, the actual manner in which the algorithm's iteration is performed is deferred to the specific iteration strategy being used.

This implies that the general structure of the iteration for a specific flavour of algorithm is constant with modifications on that algorithm being made. For example, within a Genetic Algorithm you would expect:

1. Parent individuals to be selected in some manner
2. A crossover process to be done on the selected parent individuals to create the offspring
3. A mutation process to alter the generated offspring
4. Recombine the existing parent individuals and the generated offspring to create the next generation

Parameters:

algorithm - The algorithm to perform the iteration process on.