net.sourceforge.cilib.entity.operators.crossover
Class BlendCrossoverStrategy

java.lang.Object
  net.sourceforge.cilib.entity.operators.crossover.CrossoverStrategy
      net.sourceforge.cilib.entity.operators.crossover.BlendCrossoverStrategy

All Implemented Interfaces:

Serializable, Operator, Cloneable

public class BlendCrossoverStrategy
extends CrossoverStrategy

Implementation of the blend cross-over strategy.

BibTeX entry:

 @ARTICLE{00843494,
 title={Gradual distributed real-coded genetic algorithms},
 author={{Francisco Herrera} and {Manuel Lozano}},
 journal={IEEE Trans. Evolutionary Computation},
 year={2000},
 month={April},
 volume={4},
 number={1},
 pages={ 43-63},
 abstract={ A major problem in the use of genetic algorithms is premature
   convergence, a premature stagnation of the search caused by the lack of diversity in
   the population. One approach for dealing with this problem is the distributed
   genetic algorithm model. Its basic idea is to keep, in parallel, several
   subpopulations that are processed by genetic algorithms, with each one being
   independent of the others.Furthermore, a migration mechanism produces a chromosome
   exchange between the subpopulations. Making distinctions between the subpopulations
   by applying genetic algorithms with different configurations, we obtain the
   so-called heterogeneous distributed genetic algorithms. These algorithms represent
   a promising way for introducing a correct exploration/exploitation balance in order
   to avoid premature convergence and reach approximate final solutions. This paper
   presents the gradual distributed real-coded genetic algorithms, a type of
   heterogeneous distributed real-coded genetic algorithms that apply a different
   crossover operator to each subpopulation. The importance of this operator on the
   genetic algorithm's performance allowed us to differentiate between the
   subpopulations in this fashion. Using crossover operators presented
   for real-coded genetic algorithms, we implement three instances of gradual
   distributed real-coded genetic algorithms. Experimental results show that the
   proposals consistently outperform sequential real-coded genetic algorithms and
   homogeneous distributed realcoded genetic algorithms, which are equivalent to them
   and other mechanisms presented in the literature. These proposals offer two
   important advantages at the same time: better reliability and accuracy.},
 keywords={ Crossover operator, distributed genetic algorithms, multiresolution,
   premature convergence, selective pressure},
 doi={ },
 ISSN={ }, }
 

Author:

Olusegun Olorunda

See Also:

Serialized Form

Constructor Summary

BlendCrossoverStrategy()

BlendCrossoverStrategy(BlendCrossoverStrategy copy)

Method Summary

List<Entity>	crossover(List<Entity> parentCollection)
ControlParameter	getAlpha()
BlendCrossoverStrategy	getClone() Create a cloned copy of the current object and return it.
void	performOperation(TopologyHolder holder) Perform the operator operation given the current TopologyHolder.
void	setAlpha(ControlParameter alpha)

Methods inherited from class net.sourceforge.cilib.entity.operators.crossover.CrossoverStrategy

getCrossoverProbability, getRandomNumber, getSelectionStrategy, setCrossoverProbability, setRandomNumber, setSelectionStrategy

Methods inherited from class java.lang.Object

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

Constructor Detail

BlendCrossoverStrategy

public BlendCrossoverStrategy()

BlendCrossoverStrategy

public BlendCrossoverStrategy(BlendCrossoverStrategy copy)

Method Detail

getClone

public BlendCrossoverStrategy 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 Operator

Specified by:

getClone in interface Cloneable

Specified by:

getClone in class CrossoverStrategy

Returns:

An exact clone of the current object instance.

See Also:

Object.clone()

crossover

public List<Entity> crossover(List<Entity> parentCollection)

Specified by:

crossover in class CrossoverStrategy

getAlpha

public ControlParameter getAlpha()

Returns:

the alpha

setAlpha

public void setAlpha(ControlParameter alpha)

Parameters:

alpha - the alpha to set

performOperation

public void performOperation(TopologyHolder holder)

Description copied from interface: Operator

Perform the operator operation given the current TopologyHolder.

Parameters:

holder - The TopologyHolder representing the required Topology instances.