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Marcel Kronfeld 2010-08-13 14:20:03 +00:00
parent aa3c903645
commit e708eebd93
4 changed files with 43 additions and 5 deletions
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3
resources/EvA2.props
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@ -16,3 +16,6 @@ ModulePackage = eva2.server.modules
# filter class for modules. Do not alter!
ModuleFilterClass = eva2.server.modules.AbstractModuleAdapter
# Full EvA2 version number
EvA2Version = 2.043

9
resources/FitnessConvergenceTerminator.html
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@ -7,10 +7,13 @@
<center>
</center><br>
The fitness convergence terminator stops the optimization, when there has been hardly
any change in the best population fitness (within percentual or absolute distance) for a certain
time, given in generations or fitness calls.<br>
any change in the best fitness in the population (within percentual or absolute distance) for a certain
time, given in generations or fitness calls. In case of multi-objective optimization, the 2-norm of
the fitness vector is
currently used.<br>
Be aware that, if the optimization is allowed to be non-monotonic, such as for (,)-ES strategies,
and if the optimum is close to zero, it may happen that the fitness fluctuates due to numeric
issues and does not easily converge in a relative measure.
issues and does not easily converge in a relative way.<br>
Check the help for the <a href="PopulationMeasureTerminator.html">PopulationMeasureTerminator</a> for additional information.
</body>
</html>

5
resources/PhenotypeConvergenceTerminator.html
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@ -6,10 +6,11 @@
<h1 align="center">Phenotype Convergence Terminator</h1>
<center>
</center><br>
The phenotype convergence terminator stops the optimization, when there has been hardly
The phenotype convergence terminator stops the optimization when there has been hardly
any change in the best population individual (within percentual or absolute distance) for a certain
time span, given in generations or fitness calls.<br>
Be aware that, if the optimum individual is close to zero, it may happen that its phenotype values
fluctuate due to numeric issues and do not easily converge in a relative measure.
fluctuate due to numeric issues and do not easily converge in a relative measure.<br>
Additional information is given for the superclass <a href="PopulationMeasureTerminator.html">PopulationMeasureTerminator</a>.
</body>
</html>

31
resources/PopulationMeasureTerminator.html Normal file
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@ -0,0 +1,31 @@
<html>
<head>
<title>Population Measure Terminator</title>
</head>
<body>
<h1 align="center">Population Measure Terminator</h1>
<center>
</center><br>
An abstract class giving the framework for terminators based on
a population measure converging for a given time (number of evaluations or
generations).
The class detects changes of a population P using a measure m over time and may signal convergence
if the measure m(P) behaved in a certain way for a given time. Convergence may
be signaled
<ul>
<li>if the measure reached absolute values below convThresh (absolute value),</li>
<li>if the measure remained within m(P)+/-convThresh (absolute change),</li>
<li>if the measure remained above m(P)-convThresh (absolute change and regard improvement only),</li>
<li>if the measure remained within m(P)*[1-convThresh, 1+convThresh] (relative change),</li>
<li>if the measure remained above m(P)*(1-convThresh) (relative change and regard improvement only).</li>
</ul>
The fitness convergence terminator stops the optimization, when there has been hardly
any change in the best fitness in the population (within percentual or absolute distance) for a certain
time, given in generations or fitness calls. In case of multi-objective optimization, the 2-norm of
the fitness vector is
currently used.<br>
Be aware that, if the optimization is allowed to be non-monotonic, such as for (,)-ES strategies,
and if the optimum is close to zero, it may happen that the fitness fluctuates due to numeric
issues and does not easily converge in a relative way.<br>
</body>
</html>