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Merging mk branch rev 342, generational DE.

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This commit is contained in:
Marcel Kronfeld
2009-07-23 12:34:58 +00:00
parent 4ba48274e7
commit 47877547a7
1 changed files with 69 additions and 2 deletions
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71
src/eva2/server/go/strategies/DifferentialEvolution.java
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@@ -32,6 +32,7 @@ import eva2.tools.Mathematics;
public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serializable {
private Population m_Population = new Population();
private transient Population children = null;
private AbstractOptimizationProblem m_Problem = new F1Problem();
private DETypeEnum m_DEType;
private double m_F = 0.8;
@@ -44,6 +45,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
private transient Vector<AbstractEAIndividual> parents = null;
private boolean randomizeFKLambda = false;
private boolean generational = true;
private String m_Identifier = "";
transient private InterfacePopulationChangedEventListener m_Listener;
private boolean forceRange = true;
@@ -384,7 +386,60 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
}
}
public void optimize() {
public void optimize() {
if (generational) optimizeGenerational();
else optimizeSteadyState();
}
/**
* This generational DE variant calls the method AbstractOptimizationProblem.evaluate(Population).
* Its performance may be slightly worse for schemes that rely on current best individuals,
* because improvements are not immediately incorporated as in the steady state DE.
* However it may be easier to parallelize.
*
*/
public void optimizeGenerational() {
// AbstractEAIndividual indy = null, orig;
int parentIndex;
// required for dynamic problems especially
// m_Problem.evaluatePopulationStart(m_Population);
if (children==null) children = new Population(m_Population.size());
else children.clear();
for (int i = 0; i < this.m_Population.size(); i++) {
if (cyclePop) parentIndex=i;
else parentIndex=RNG.randomInt(0, this.m_Population.size()-1);
AbstractEAIndividual indy = generateNewIndividual(m_Population, parentIndex);
children.add(indy);
}
m_Problem.evaluate(children);
int nextDoomed = getNextDoomed(m_Population, 0);
for (int i = 0; i < this.m_Population.size(); i++) {
AbstractEAIndividual indy = children.getEAIndividual(i);
if (cyclePop) parentIndex=i;
else parentIndex=RNG.randomInt(0, this.m_Population.size()-1);
if (nextDoomed >= 0) { // this one is lucky, may replace an 'old' one
m_Population.replaceIndividualAt(nextDoomed, indy);
nextDoomed = getNextDoomed(m_Population, nextDoomed+1);
} else {
if (m_Problem instanceof AbstractMultiObjectiveOptimizationProblem) {
ReplacementCrowding repl = new ReplacementCrowding();
repl.insertIndividual(indy, m_Population, null);
} else {
// index = RNG.randomInt(0, this.m_Population.size()-1);
if (!compareToParent) parentIndex = RNG.randomInt(0, this.m_Population.size()-1);
AbstractEAIndividual orig = (AbstractEAIndividual)this.m_Population.get(parentIndex);
if (indy.isDominatingDebConstraints(orig)) this.m_Population.replaceIndividualAt(parentIndex, indy);
}
}
}
this.m_Population.incrFunctionCallsBy(children.size());
this.m_Population.incrGeneration();
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
public void optimizeSteadyState() {
AbstractEAIndividual indy = null, orig;
int index;
@@ -458,7 +513,7 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
this.m_Population.incrGeneration();
this.firePropertyChangedEvent(Population.nextGenerationPerformed);
}
/**
* Search for the first individual which is older than the age limit and return its index.
* If there is no age limit or all individuals are younger, -1 is returned. The start index
@@ -714,4 +769,16 @@ public class DifferentialEvolution implements InterfaceOptimizer, java.io.Serial
public String compareToParentTipText() {
return "Compare a challenge individual to its original parent instead of a random one.";
}
public boolean isGenerational() {
return generational;
}
public void setGenerational(boolean generational) {
this.generational = generational;
}
public String generationalTipText() {
return "Switch to generational DE as opposed to standard steady-state DE";
}
}