Processor thread now has a name.
Fix several html files.
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@ -8,4 +8,4 @@ EvA2 integrates several derivation free optimization methods, preferably populat
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<p>EvA2 aims at two groups of users. Firstly, the end user who does not know much about the theory of Evolutionary Algorithms, but wants to use Evolutionary Algorithms to solve an application problem. Secondly, the scientific user who wants to investigate the performance of different optimization algorithms or wants to compare the effect of alternative or specialized evolutionary or heuristic operators. The latter usually knows more about evolutionary algorithms or heuristic optimization and is able to extend EvA2 by adding specific optimization strategies or solution representations.</p>
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<p>EvA2 is being used as teaching aid in lecture tutorials, as a developing platform in student research projects and applied to numerous optimisation problems within active research and ongoing industrial cooperations.</p>
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<body>
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</body>
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3
resources/META-INF/MANIFEST.MF
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3
resources/META-INF/MANIFEST.MF
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@ -0,0 +1,3 @@
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Manifest-Version: 1.0
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Main-Class: eva2.gui.Main
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@ -5,7 +5,7 @@
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<body>
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<h1 align="center">Schwefel's (sine root) function</h1>
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<center>
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<img src="../images/f13-tex-500.jpg" width="650" height="64" aling="center">
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<img src="../images/f13-tex-500.jpg" width="650" height="64" align="center">
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</center>
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<p>
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Schwefel's (sine root) function is highly multimodal and has no global basin of attraction. The optimum at a fitness of f(x*)=0 lies at x*=420.9687. Schwefel's sine root is a tough challenge for any global optimizer due to the multiple distinct optima. Especially, there is a deceptive nearly optimal solution close to x=(-420.9687)<SUP>n</SUP>.
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@ -5,7 +5,7 @@
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<body>
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<h1 align="center">The Step Function</h1>
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<center>
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<img src="../images/steptex.jpg" width="350" height="120" aling="center">
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<img src="../images/steptex.jpg" width="350" height="120" align="center">
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</center>
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<p>
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The idea of this function is the implementation of a flat plateau (slope 0)in an underlying continuous function. It's harder for optimization algortihms to find optima because minor changes of the object variables don't affect the fitness. Therefore no conclusions about the search direction can be made.
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@ -5,7 +5,7 @@
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<body>
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<h1 align="center">Ackley's function</h1>
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<center>
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<img src="../images/ackleytex.jpg" width="500" height="58" aling="center">
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<img src="../images/ackleytex.jpg" width="500" height="58" align="center">
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</center>
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<p>
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Ackley's function is multimodal and symmetrical. It is based on an exponential function and modulated by a cosine function.
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@ -5,7 +5,6 @@
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<body>
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<h1 align="center">Generic Constraints</h1>
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<br>
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<p>To represent generic constraints on real-valued functions, this class can parse
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String expressions in prefix notation of the form:
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<blockquote>
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@ -37,7 +37,7 @@ class ArgumentTree extends LinkedHashMap<String, Object> {
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}
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/**
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* If there are no key, value pairs present and the value is unset,
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* If there are no key/value pairs present and the value is unset,
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* this tree belongs to a flag.
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*
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* @return
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@ -61,6 +61,7 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
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optimizationStateListener = module;
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}
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/**
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* Construct a Processor instance and make statistics instance informable of
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* the parameters, such they can by dynamically show additional information.
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@ -165,13 +166,14 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
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public Population runOptimizationOnce() {
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try {
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EVAERROR.clearMsgCache();
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this.setName(getInfoString());
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while (isOptimizationRunning()) {
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setPriority(3);
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if (saveParams) {
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try {
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optimizationParameters.saveInstance();
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} catch (Exception e) {
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System.err.println("Error on saveInstance!");
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LOGGER.log(Level.WARNING, "Could not save optimization instance!", e);
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}
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}
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resultPopulation = this.optimize();
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@ -194,6 +196,8 @@ public class Processor extends Thread implements InterfaceProcessor, InterfacePo
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optimizationStateListener.performedStop(); // is only needed in client server mode
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optimizationStateListener.updateProgress(0, errMsg);
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}
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} finally {
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this.setName("Optimization Processor");
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}
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return resultPopulation;
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}
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