Fabian Becker
b1553f3088
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31 lines
1.6 KiB
HTML
31 lines
1.6 KiB
HTML
<html>
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<head>
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<title>Population Measure Terminator</title>
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</head>
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<body>
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<h1 align="center">Population Measure Terminator</h1>
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<center>
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</center><br>
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An abstract class giving the framework for terminators based on
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a population measure converging for a given time (number of evaluations or
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generations).
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The class detects changes of a population P using a measure m over time and may signal convergence
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if the measure m(P) behaved in a certain way for a given time. Convergence may
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be signaled
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<ul>
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<li>if the measure reached absolute values below convThresh (absolute value),</li>
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<li>if the measure remained within m(P)+/-convThresh (absolute change),</li>
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<li>if the measure remained above m(P)-convThresh (absolute change and regard improvement only),</li>
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<li>if the measure remained within m(P)*[1-convThresh, 1+convThresh] (relative change),</li>
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<li>if the measure remained above m(P)*(1-convThresh) (relative change and regard improvement only).</li>
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</ul>
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The fitness convergence terminator stops the optimization, when there has been hardly
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any change in the best fitness in the population (within percentual or absolute distance) for a certain
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time, given in generations or fitness calls. In case of multi-objective optimization, the 2-norm of
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the fitness vector is
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currently used.<br>
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Be aware that, if the optimization is allowed to be non-monotonic, such as for (,)-ES strategies,
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and if the optimum is close to zero, it may happen that the fitness fluctuates due to numeric
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issues and does not easily converge in a relative way.<br>
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</body>
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</html> |