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//=============================================================================
// Copyright 2006-2010 Daniel W. Dyer
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//=============================================================================
package org.uncommons.watchmaker.framework.selection;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import java.util.Random;
import org.uncommons.watchmaker.framework.EvaluatedCandidate;
import org.uncommons.watchmaker.framework.SelectionStrategy;
/**
* <p>A selection strategy that is similar to fitness-proportionate selection
* except that is uses relative fitness rather than absolute fitness in order to
* determine the probability of selection for a given individual (i.e. the actual
* numerical fitness values are ignored and only the ordering of the sorted
* population is considered).</p>
* <p>Rank selection is implemented in terms of a mapping function ({@link
* #mapRankToScore(int, int)}) and delegation to a fitness-proportionate selector. The
* mapping function converts ranks into relative fitness scores that are used to
* drive the delegate selector.</p>
* @author Daniel Dyer
*/
public class RankSelection implements SelectionStrategy<Object>
{
private final SelectionStrategy<Object> delegate;
/**
* Creates a default rank-based selector with a linear
* mapping function and selection frequencies that correspond
* to expected values.
*/
public RankSelection()
{
this(new StochasticUniversalSampling());
}
/**
* Creates a rank-based selector with a linear mapping function and
* configurable delegate for performing the proportionate selection.
* @param delegate The proportionate selector that will be delegated
* to after converting rankings into relative fitness scores.
*/
public RankSelection(SelectionStrategy<Object> delegate)
{
this.delegate = delegate;
}
/**
* {@inheritDoc}
*/
public <S> List<S> select(List<EvaluatedCandidate<S>> population,
boolean naturalFitnessScores,
int selectionSize,
Random rng)
{
List<EvaluatedCandidate<S>> rankedPopulation = new ArrayList<EvaluatedCandidate<S>>(population.size());
Iterator<EvaluatedCandidate<S>> iterator = population.iterator();
int index = -1;
while (iterator.hasNext())
{
S candidate = iterator.next().getCandidate();
rankedPopulation.add(new EvaluatedCandidate<S>(candidate,
mapRankToScore(++index,
population.size())));
}
return delegate.select(rankedPopulation, true, selectionSize, rng);
}
/**
* <p>Maps a population index to a relative pseudo-fitness score that can be used for
* fitness-proportionate selection. The general contract for the mapping function
* {@code f} is: {@code f(rank) >= f(rank + 1)} for all legal values of
* {@code rank}, assuming natural scores.</p>
* <p>The default mapping function is a simple linear transformation, but this
* can be over-ridden in sub-classes. Alternative implementations can be linear or
* non-linear and either natural or non-natural.</p>
* @param rank A zero-based index into the population
* {@code (0 <= rank < populationSize)}.
* @param populationSize The number of individuals in the population.
* @return {@code populationSize - rank}
*/
protected double mapRankToScore(int rank, int populationSize)
{
return populationSize - rank;
}
/**
* {@inheritDoc}
*/
@Override
public String toString()
{
return "Rank Selection";
}
}
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