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Importing release version 322 from old repos

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Marcel Kronfeld
2007-12-11 16:38:11 +00:00
parent 8cecdb016d
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<html>
<head>
<title>Convergence Terminator</title>
</head>
<body>
<h1 align="center">Convergence Terminator</h1>
<center>
</center><br>
The convergence terminator stops the optimization, when there has been hardly
any change in the best population fitness (within percentual range) for a certain
time, given in generations or fitness calls.
</body>
</html>

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<html>
<head>
<title>Default page</title>
</head>
<body>
<EFBFBD>
<h1 align="center">HTML description file is missing</h1>
<center>
</center><br>
Unfortunately there is no additional HTML description
file to this class. Please refer to the JOptDocumentation
file or the JavaDoc for more information on this class.
</body>
</html>

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<html>
<head>
<title>Epsilon SV-Regression</title>
</head>
<body>
<EFBFBD>
<h1 align="center">Epsilon SV-Regression</h1>
<center>
</center><br>
Please read the JavaEvA manual for a detailed description.
</ul>
</body>
</html>

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<html>
<head>
<title>ESIndividual</title>
</head>
<body>
<EFBFBD>
<h1 align="center">ESIndividual</h1>
<center>
</center><br>
This element represents the properties of an individual.
The most important evolutionary operator of an ES is
the mutation of the objective variables representing
the solution of the problem, which is responsible
for the self-adaptation capability of the ES
</body>
</html>

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<html>
<head>
<title>f_1 : Sphere function</title>
</head>
<body>
<EFBFBD>
<h1 align="center">ESInitPopulationSpaceFilling</h1>
<center>
</center><br>
ESPara contains the information describing the Evolution Strategy:
<ul>
<li>The problem to be solved.</li>
<li>A seed value for the random number genarator.</li>
<li>A termination criterium for the algorithm.</li>
<li>The used population.</li>
</ul>
</body>
</html>

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<html>
<head>
<title>ESInitPopulationRandom</title>
</head>
<body>
<EFBFBD>
<h1 align="center">ESInitPopulationRandom</h1>
<center>
</center><br>
Here you can specify the number of individuals, which are randomly initialized.

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<html>
<head>
<title>f_1 : Sphere function</title>
</head>
<body>
<EFBFBD>
<h1 align="center">ESInitPopulationSpaceFilling</h1>
<center>
</center><br>
ESPara contains the information describing the Evolution Strategy:
<ul>
<li>The problem to be solved.</li>
<li>A seed value for the random number genarator.</li>
<li>A termination criterium for the algorithm.</li>
<li>The used population.</li>
</ul>
</body>
</html>

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<html>
<head>
<title>f_1 : Sphere function</title>
</head>
<body><EFBFBD>
<h1 align="center">Parameters for the Evolution Strategy</h1>
<center>
</center><br>
The Java Class ESPara contains the information describing an Evolution Strategy (ES):
<ul>
<li>The problem to be solved.</li>
<li>A seed value for the random number generator.</li>
<li>A termination criterion for the algorithm.</li>
<li>The ES population.</li>
</ul>
</body>
</html>

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<html>
<head>
<title>ESPopulation</title>
</head>
<body><EFBFBD>
<h1 align="center">ESPopulation</h1>
<center>
</center><br>
ESPopulation contains the information describing an Evolution Strategy (ES):
<ul>
<li>A prototype of an individual (contains mutation operator).</li>
<li>The population size of the parents: lambda.</li>
<li>The population size of the children: mu.</li>
<li>A recombination operator.</li>
<li>A fitness based selection operator.</li>
</ul>
</body>
</html>

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<html>
<head>
<title>ESRecombination</title>
</head>
<body>
<EFBFBD>
<h1 align="center">ESRecombination</h1>
<center>
</center><br>
The recombination operator has the following editable properties:
<ul>
<li>strategy for recombination of the strategy parameters of the mutation operators..</li>
<li>strategy for recombination of the objectives of an individual..</li>
<li>rho = number of parents, which recombinate to one offspring individual..</li>
<li>strategy for selecting the input individuals for one recombination.</li>
</ul>
</body>
</html>

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<html>
<head>
<title>ES Comma Selection</title>
</head>
<body>
<EFBFBD>
<h1 align="center">ES Comma Selection Operator</h1>
<center>
</center><br>
The best mu individuals are selected from lambda offspring individuals.
</body>
</html>

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<html>
<head>
<title>ES - Median Selection </title>
</head>
<body>
<EFBFBD>
<h1 align="center">ES Median Selection Strategy</h1>
<center>
</center><br>
The main application field of the Median Selection Strategy
operator are steady state algorithms.
A standard steady-state ES is equivalent to a (mu + 1) ES.
Only one individual is generated and evaluated
at each step and gets immediately integrated into the population.
Compared to generation based algorithms the information of
new evaluated individuals can be integrated directly into the optimization process.
The idea is to approximate the selection mechanism
of a standard (mu,lambda) ES, by
using a fitness buffer containing
fitness values of the last n evaluations.
Given a relative rate of acceptance r=mu\lambda.
A newly evaluated individual substitutes the worst individual
of the population, if it has a better fitness than the r*n best individuals
in the buffer.
</body>
</html>

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<html>
<head>
<title>f_1 : Sphere function</title>
</head>
<body>
<EFBFBD>
<h1 align="center">ESSelectionStrategyMedian</h1>
<center>
</center><br>
ESPara contains the information describing the Evolution Strategy:
<ul>
<li>The problem to be solved.</li>
<li>A seed value for the random number genarator.</li>
<li>A termination criterium for the algorithm.</li>
<li>The used population.</li>
</ul>
</body>
</html>

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<html>
<head>
<title>Plus Selection Strategy</title>
</head>
<body>
<EFBFBD>
<h1 align="center">ES Plus Selection Operator</h1>
<center>
</center><br>
The best mu individuals are selected from the
aggregation of the lambda offspring individuals and the mu parents.
</body>
</html>

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<html>
<head>
<title>Evolution Strategy - ES</title>
</head>
<body>
<EFBFBD>
<h1 align="center">Evolution Strategy - ES</h1>
<center>
</center><br>
An ES works on a population of real valued solutions
by repeated use of evolutionary operators like reproduction,
recombination and mutation (see pseudocode in figures.
lambda offspring individuals are generated from mu parents
by recombination and mutation. After evaluating the fitness of the lambda
offspring individuals, mu individuals with the best fitness are
selected by a comma-strategy to build the parent population for the next generation.
On the other hand, a plus-strategy selects the best mu individuals
from the aggregation of parents and offspring individuals.
The properties of ES are given in the population sub frame.
</body>
</html>

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<html>
<head>
<title>f_1 : Sphere function</title>
</head>
<body><EFBFBD>
<h1 align="center">The Sphere function</h1>
<center>
<img src="images/f1tex.jpg" width="85" height="95" border="0" align="center">
</center><br>
The sphere function is a <i>n</i>-dimensional, axis-symmetric, continuously differentiable, convex function:
<p>
Because of its simplicity every optimization-algorithm should be able to find its global minimum at <i>x</i>=[0, 0, ... , 0]
<p>
<img src="images/f1.jpg" width="480" height="360" border="2" align="middle">
<hr>
More information about the sphere function can be found at:
<p>
Kenneth De Jong. <i>An analysis of the behaviour of a class of genetic adaptive systems.</i> Dissertation, University of Michigan, 1975. Diss. Abstr. Int. 36(10), 5140B, University Microflims No. 76-9381.
</body>
</html>

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<html>
<head>
<title>Generalized Rosenbrock's function</title>
</head>
<body><EFBFBD>
<h1 align="center">Generalized Rosenbrock's function</h1>
<center>
<img src="images/rosenbrocktex.jpg" width="500" height="78">
</center>
<p>
This function i unimodal and continuous, but the global optimum is hard to find, because of independence through the term (<i>x</i>_(<i>i</i>+1) - <i>x_i</i>*<i>x_i</i>)^2 between contiguous parameters.
<p>
<img src="images/f85.jpg" border="2">
<br>
Rosenbrock's function within the co-domain -5 <= <i>x</i> <= 5.
<p>
The global optimum is located in a prabolic formed valley (among the curve x^2 = x_1^2), which has a flatten ground.
<br>
<img src="images/f81.jpg" border="2">
<br>
The function close to its global optimum, which is: f(<i>x</i>) = f(1, 1, ... , 1) = 0.
<p>
Rosenbrock' function is not symmetric, not convex and not linear.
<hr>
More information about Rosenbrock's function can be found at:
<p>
Kenneth De Jong. <i>An analysis of the behaviour of a class of genetic adaptive systems.</i> Dissertation, University of Michigan, 1975. Diss. Abstr. Int. 36(10), 5140B, University Microflims No. 76-9381.
<p>
Hans Paul Schwefel. <i>Evolution and optimum seeking.</i> Sixth-Generation Computer Technology Series. John Wiley & Sons, INC., 1995.
<p>
Darrell Whitley, Soraya Rana, John Dzubera, Keith E. Mathias. <i>Evaluating Evolutionary Algorithms. Artificial Intelligence</i>, 85(1-2):245-276. 1996.
<p>
Eberhard Schoeneburg, Frank Heinzmann, Sven Feddersen. <i>Genetische Algorithmen und Evolutionstrategien - Eine Einfuehrung in Theorie und Praxis der simulierten Evolution.</i> Addison-Wesley, 1994.
</body>
</html>

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<html>
<head>
<title>The step function</title>
</head>
<body><EFBFBD>
<h1 align="center">The step function</h1>
<center>
<img src="images/steptex.jpg" width="350" height="120" aling="center">
</center>
<p>
The idea of this function is the implementation of a flat plateau (slope 0)in an underlying continuous function.Its harder for optimization algortihms to find optimums because minor changes of the object variables don't affect the fitness. Therefore no conclusions about the search direction can be made.
<p>
<img src="images/step5.jpg" width="480" height="360" border="2" align="center">
<p>
The step function is symmetric considering the underlying function (here: f(x,y) = f(y,x)), but between the bulk constant plateau-areas not continuously differentiable.
<p>
Its minimum-area is located in the intervalls: <i>f(x)</i>=<i>f</i>([-5.12,-5), ... , [-5.12,-5))=0.
<p>
<img src="images/stepopt.jpg" width="480" height="360" border="2" align="center">
<hr>
More information about the step function can be found at:
<p>
Thomas Baeck, <i>Evolutionary Algorithms in Theory and Practice.</i> Oxford University Press, 1996.
<p>
Darrell Whitley, Soraya Rana, John Dzubera, Keith E. Mathias. <i>Evaluating Evolutionary Algorithms. Artificial Intelligence</i>, 85(1-2):245-276. 1996.
<p>
Eberhard Schoeneburg, Frank Heinzmann, Sven Feddersen. <i>Genetische Algorithmen und Evolutionstrategien - Eine Einfuehrung in Theorie und Praxis der simulierten Evolution.</i> Addison-Wesley, 1994.
</body>
</html>

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<html>
<head>
<title>Schwefel's double sum</title>
</head>
<body><EFBFBD>
<h1 align="center">Schwefels double sum</h1>
<center>
<img src="images/f2tex.jpg" width="220" height="102" border="0" align="center">
</center>
<p>
Schwefel's double sum is a quadratic minimization problem which difficulty increases by the dimension <i>n</i> in <i>O(n<>)</i>. It is used for analysis of correlating mutations.
<p>
It possesses specific symmetrical properties:<br>
<img src="images/schwefelsymmetrie.jpg" width="500" height="104" border="0" align="middle">
<p>
Its minimum is located at: <i>f(x)</i>=<i>f</i>([0, 0, ... , 0])=0
<p>
<img src="images/f2.jpg" width="480" height="360" border="2" align="middle">
<hr>
More information about Schwefel's double sum can be found at:
<p>
Hans Paul Schwefel. <i>Evolution and optimum seeking.</i> Sixth-Generation Computer Technology Series. John Wiley & Sons, INC., 1995.
</body>
</html>

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<html>
<head>
<title>Generalized Rastrigin's function</title>
</head>
<body><EFBFBD>
<h1 align="center">Generalized Rastrigin's function</h1>
<center>
<img src="images/rastrigintex.jpg" width="500" height="101">
</center>
<p>
Rastrigin's function is symmetric. It is based on the simple <i>sphere function</i> (called f_1 in the JavaEva<76> context), but it is multimodal because a modulation term on the basis of the cosine function is added. This evokes hills and valleys which are misleading local optimums.
<p>
Values are used for the following illustrations: <i>A</i>=10, <i>&#969;</i>=2*&#960;, <i>n</i>=2.
<br>
<img src="images/rastrigin20.jpg" border="2">
<br>
Rastrigin's function within the co-domain -20>=<i>x</i>>=20
<p>
<img src="images/rastrigin5.jpg" border="2">
<br>
Rastrigin's function within the co-domain -5>=<i>x</i>>=5
<p>
Like Ackley's function a simple evolutionary algorithm would get stuck in a local optimum, while a broader searching algorithm would get out of the local optimum closer to the global optimum, which in this case is: f(<i>x</i>) = f(0, 0, ... , 0) = 0.
<p>
<img src="images/rastrigin1.jpg" border="2"><br>
Rastrigin's function close to its optimum.
<hr>
More information about Rastrigin's function can be found at:
<p>
Darrell Whitley, Soraya Rana, John Dzubera, Keith E. Mathias. <i>Evaluating Evolutionary Algorithms. Artificial Intelligence</i>, 85(1-2):245-276. 1996.
<p>
Eberhard Schoeneburg, Frank Heinzmann, Sven Feddersen. <i>Genetische Algorithmen und Evolutionstrategien - Eine Einfuehrung in Theorie und Praxis der simulierten Evolution.</i> Addison-Wesley, 1994.
</body>
</html>

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<html>
<head>
<title>Ackley's function</title>
</head>
<body><EFBFBD>
<h1 align="center">Ackley's function</h1>
<center>
<img src="images/ackleytex.jpg" width="500" height="58" aling="center">
</center>
<p>
Ackley's function is intense multimodal and symmetrical. It refers to an exponential function which is modulated through a cosine function. The outside region is almost planar by the growing influence of the exponential function. In the center it possesses a hole by the influence of the cosine function.<br>
Its minimum is at: <i>f(x)</i>=<i>f</i>([0, 0, ... , 0])=0.
<p>
The difficulty for an optmization algorithm is mid-graded because a simple optimization-algorithm like <i>hill-climbing</i> would get stuck in a local minimum. The optimization algorithm has to search a broader environ to overcome the local minimum and get closer to the global optima.
<p>
<img src="images/ackley.jpg" width="480" height="360" border="2" align="center">
<br>
Ackley's function within the co-domain -20 >= <i>x</i> >= 20, <i>a</i>=20, <i>b</i>=0.2, <i>c</i>=2*&#960;, <i>n</i>=2.
<p>
<img src="images/ackleyopt.jpg" width="480" height="360" border="2" align="center">
<br>
Ackley's function close to the optimum.
<hr>
More information about Ackley's function can be found at:
<p>
David. H. Ackley. <i>A connection machine for genetic hillclimbing.</i> Kluwer Academic Publishers, Boston, 1987.
<p>
Thomas Baeck. <i>Evolutionary Algorithms in Theory and Practice.</i> Oxford University Press, 1996.
</body>
</html>

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<html>
<head>
<title>JavaEvA Genetic Optimization</title>
</head>
<body>
<h1 align="center">The JavaEvA Genetic Optimization Module</h1>
<br>
The Genetic Optimization module allows the application of a variety of
nature-inspired heuristics within one framework. You can combine several
datatypes as representations with specific evolutionary operators and
widely independently choose an optimization strategy. Some strategies,
however, only work with certain datatypes. Most remarkably, DE and PSO
require a real-valued representations for the moment, whereas GA, for example,
is typically run with a binary datatype but also works on real valued individuals
by just accessing the analoguous evolutionary operators.
</body>
</html>

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<html>
<head>
<title>JavaEvA Genetic Optimization</title>
</head>
<body>
<h1 align="center">Genetic Optimization Parameters</h1>
<br>
The GO parameter class is used to change main GO optimization settings. You may:
<ul>
<li>Choose the optimizer. Check the optimizer object for further parameters and information.</li>
<li>Choose an output file name. If "none" is used, no output file will be written.</li>
<li>Select the problem to be optimized. Check the problem instance for further parameters and information. </li>
<li>Set a random seed. For the same seed, an optimization run should yield the same results. Set the seed to zero to use a dynamic seed for each run (using system time).</li>
<li>Define the termination criterion. Usually a maximum number of fitness evaluations is set, but
it is also possible to choose a maximum number of generations, an absolute fitness value to be reached, a
convergence criterion measured in fitness change over time, or a combination of those.</li>
</ul>
<b>Note:</b> <br>
The evolutionary operators used by a strategy are tightly connected to the representation used.
On the other hand, the representation is usually defined by the underlying problem, therefore,
to change the operators effecting the individuals, select the problem and set them within the
Individual class presented there. Also note, that not all optimizers can handle all types
of representations.
</body>
</html>

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<html>
<head>
<title>Gauss Process Regression Model</title>
</head>
<body>
<EFBFBD>
<h1 align="center">Gauss Process Regression Model</h1>
<center>
</center><br>
Please read the JavaEvA manual for a detailed description.
</ul>
</body>
</html>

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# Select a default module. Set empty or comment out to select from all available modules.
#
# possible values are the names of module objects as returned by their getName method (!).
# DefaultModule = Evolution_Strategy
DefaultModule = Genetic_Optimization
## Uncomment this to show all loadable modules. Most are redundant, though.
ShowModules
ServerList = localhost,134.2.172.14,ranode22
# base class for modules. Do not alter!
ModulePackage = javaeva.server.modules
# filter class for modules. Do not alter!
ModuleFilterClass = javaeva.server.modules.AbstractModuleAdapter
###################### The GO part ######################################
# there are no further props necessary

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<html>
<head>
<title>Model Assisted Evolution Strategy - MAES</title>
</head>
<body>
<EFBFBD>
<h1 align="center">Model Assisted Evolution Strategy - MAES</h1>
<center>
</center><br>
In the pre-selection concept lambdaPlus>lambda
individuals are generated from mu parents.
All lambdaPlus individuals are evaluated by a
surrogate model of the fitness landscape and the estimated
fitness values are used to pre-select the lambda
best individuals, which will be evaluated with
the real fitness function.
The model is trained at the beginning with a randomly created
initial population and is updated after each generation
step with lambda new fitness cases.
The idea behind this approach is that only the
most promising individuals with a good fitness prediction
are evaluated with the true fitness function.
Every generation a new offspring lambda is evaluated with the real fitness function,
the model is updated with this information of \lambda fitness cases.
</body>
</html>

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<html>
<head>
<title>MAESIndividual</title>
</head>
<body>
<EFBFBD>
<h1 align="center">MAESIndividual</h1>
<center>
</center><br>
This element represents the properties of an individual.
The most important evolutionary operator of an ES is
the mutation of the objective variables representing
the solution of the problem, which is responsible
for the self-adaptation capability of the ES
</body>
</html>

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<html>
<head>
<title>MAESPara</title>
</head>
<body><EFBFBD>
<h1 align="center">MAESPara</h1>
<center>
</center><br>
MAESPara contains the information describing an Evolution Strategy (ES):
<ul>
<li>The problem to be solved.</li>
<li>A seed value for the random number generator.</li>
<li>A termination criterion for the algorithm.</li>
<li>The MAES population.</li>
</ul>
</body>
</html>

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<html>
<head>
<title>MAESPopulation</title>
</head>
<body><EFBFBD>
<h1 align="center">Model Assisted Population</h1>
<center>
</center><br>
The MAESPopulation panel contains the information describing the Model Assisted Evolution Strategy (MAES):
<ul>
<li>A prototype of an individual (contains mutation operator).</li>
<li>The size of the model pre-selected individuals: lambdaPlus>=lambda.
For lambdaPlus=lambda you have no model impact.</li>
<li>The regression model for fitness prediction.</li>
<li>The model size is given by the number of last evaluated individuals,
which are used to train the model.</li>
<li>The population size of the parents: lambda.</li>
<li>The population size of the children: mu.</li>
<li>A recombination operator.</li>
<li>A fitness based selection operator.</li>
</ul>
</body>
</html>

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<html>
<head>
<title>MAESRecombination</title>
</head>
<body>
<EFBFBD>
<h1 align="center">MAESRecombination</h1>
<center>
</center><br>
The recombination operator has the following editable properties:
<ul>
<li>strategy for recombination of the strategy parameters of the mutation operators..</li>
<li>strategy for recombination of the objectives of an individual..</li>
<li>rho = number of parents, which recombinate to one offspring individual..</li>
<li>strategy for selecting the input individuals for one recombination.</li>
</ul>
</body>
</html>

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<html>
<head>
<title>MAES Comma Selection</title>
</head>
<body>
<EFBFBD>
<h1 align="center">MAES Comma Selection Operator</h1>
<center>
</center><br>
The best mu individuals are selected from lambda offspring individuals.
</body>
</html>

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<html>
<head>
<title>MAES - Median Selection </title>
</head>
<body>
<EFBFBD>
<h1 align="center">MAES Median Selection Strategy</h1>
<center>
</center><br>
The main application field of the Median Selection Strategy
operator are steady state algorithms.
A standard steady-state ES is equivalent to a (mu + 1) ES.
Only one individual is generated and evaluated
at each step and gets immediately integrated into the population.
Compared to generation based algorithms the information of
new evaluated individuals can be integrated directly into the optimization process.
The idea is to approximate the selection mechanism
of a standard (mu,lambda) ES, by
using a fitness buffer containing
fitness values of the last n evaluations.
Given a relative rate of acceptance r=mu\lambda.
A newly evaluated individual substitutes the worst individual
of the population, if it has a better fitness than the r*n best individuals
in the buffer.
<ul>
<li>The problem to be solved.</li>
<li>A seed value for the random number genarator.</li>
<li>A termination criterium for the algorithm.</li>
<li>The used population.</li>
</ul>
</body>
</html>

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<html>
<head>
<title>Plus Selection Strategy</title>
</head>
<body>
<EFBFBD>
<h1 align="center">MAES Plus Selection Operator</h1>
<center>
</center><br>
The best mu individuals are selected from the
aggredation of the lambda offspring individuals and the mu parents.
</body>
</html>

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0.8160000000000001 0.3341439999999999
0.8180000000000001 0.33087599999999995
0.8200000000000001 0.3275999999999999
0.8220000000000001 0.32431599999999994
0.8240000000000001 0.32102399999999987
0.8260000000000001 0.3177239999999999
0.8280000000000001 0.3144159999999999
0.8300000000000001 0.31109999999999993
0.8320000000000001 0.3077759999999998
0.834 0.30444400000000005
0.836 0.30110400000000004
0.838 0.297756
0.84 0.2944000000000001
0.842 0.2910360000000001
0.844 0.28766400000000003
0.846 0.2842840000000001
0.848 0.28089600000000003
0.85 0.2775000000000001
0.852 0.274096
0.854 0.27068400000000004
0.856 0.26726400000000006
0.858 0.26383600000000007
0.86 0.2604000000000001
0.862 0.2569560000000001
0.864 0.25350400000000006
0.866 0.25004400000000004
0.868 0.24657600000000002
0.87 0.24309999999999998
0.872 0.23961600000000005
0.874 0.236124
0.876 0.23262400000000005
0.878 0.229116
0.88 0.22560000000000002
0.882 0.22207599999999994
0.884 0.21854399999999996
0.886 0.21500399999999997
0.888 0.21145599999999998
0.89 0.20789999999999997
0.892 0.20433599999999996
0.894 0.20076399999999994
0.896 0.19718399999999991
0.898 0.193596
0.9 0.18999999999999995
0.902 0.186396
0.904 0.18278399999999995
0.906 0.179164
0.908 0.17553599999999991
0.91 0.17189999999999994
0.912 0.16825599999999996
0.914 0.16460399999999997
0.916 0.16094399999999998
0.918 0.15727599999999997
0.92 0.15359999999999996
0.922 0.14991599999999994
0.924 0.1462239999999999
0.926 0.14252399999999987
0.928 0.13881599999999994
0.93 0.1350999999999999
0.932 0.13137599999999994
0.934 0.12764399999999987
0.936 0.1239039999999999
0.9380000000000001 0.12015599999999993
0.9400000000000001 0.11639999999999984
0.9420000000000001 0.11263599999999985
0.9440000000000001 0.10886399999999985
0.9460000000000001 0.10508399999999984
0.9480000000000001 0.10129599999999983
0.9500000000000001 0.09749999999999992
0.9520000000000001 0.09369599999999989
0.9540000000000001 0.08988399999999985
0.9560000000000001 0.08606399999999981
0.9580000000000001 0.08223599999999986
0.96 0.07840000000000003
0.962 0.07455600000000007
0.964 0.0707040000000001
0.966 0.06684400000000001
0.968 0.06297600000000003
0.97 0.05910000000000004
0.972 0.05521600000000004
0.974 0.051324000000000036
0.976 0.04742400000000002
0.978 0.043516
0.98 0.03960000000000008
0.982 0.03567600000000004
0.984 0.031743999999999994
0.986 0.02780400000000005
0.988 0.02385599999999999
0.99 0.01990000000000003
0.992 0.01593600000000006
0.994 0.011963999999999975
0.996 0.007983999999999991
0.998 0.0039959999999999996
1.0 0.0

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<html>
<head>
<title>CMA Mutation</title>
</head>
<body>
<EFBFBD>
<h1 align="center">CMA Mutation</h1>
<center>
</center><br>
Please read the JavaEvA manual for a detailed description.
</ul>
</body>
</html>

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<html>
<head>
<title>f_1 : Sphere function</title>
</head>
<body>
<EFBFBD>
<h1 align="center">MutationMSRGlobal</h1>
<center>
</center><br>
Please read the JavaEvA manual for a detailed description.
</ul>
</body>
</html>

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<html>
<head>
<title>f_1 : Sphere function</title>
</head>
<body>
<EFBFBD>
<h1 align="center">MutationMSRSeperate</h1>
<center>
</center><br>
<center>
</center><br>
Please read the JavaEvA manual for a detailed description.
</ul>
</body>
</html>

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<html>
<head>
<title>MVA Mutation</title>
</head>
<body>
<EFBFBD>
<h1 align="center">MVA Mutation</h1>
<center>
</center><br>
Please read the JavaEvA manual for a detailed description.
</ul>
</body>
</html>

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<html>
<head>
<title>Random Mutation</title>
</head>
<body>
<EFBFBD>
<h1 align="center">Random Mutation</h1>
<center>
</center><br>
Please read the JavaEvA manual for a detailed description.
</ul>
</body>
</html>

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<html>
<head>
<title>Success Rule Mutation</title>
</head>
<body>
<EFBFBD>
<h1 align="center">Success Rule</h1>
<center>
</center><br>
Please read the JavaEvA manual for a detailed description.
</ul>
</body>
</html>

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<html>
<head>
<title>NU SV-Regression</title>
</head>
<body>
<EFBFBD>
<h1 align="center">NU SV-Regression</h1>
<center>
</center><br>
Please read the JavaEvA manual for a detailed description.
</ul>
</body>
</html>

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<html>
<head>
<title>Particle Swarm Optimization - PSO</title>
</head>
<body>
<h1 align="center">Particle Swarm Optimization - PSO</h1>
<center>
</center><br>
The Particle Swarm Optimization by Kennedy and Eberhardt is inspired by swarm intelligent
behaviour seen in animals like birds or ants. A swarm of particles is a set of individual agents
"flying" across the search space with individual velocity vectors. There is no selection as in
classic Evolutionary Algorithms. Instead, the individuals exchange knowledge about the space they
have come across. Each one is attracted to the best position the individual has seen so far (cognitive
component) and to the best position known by its neighbors (social component).<br>
The neighborhood is defined by the swarm velocity, which may be a linear ordering, a grid and some others.
The influence of the velocity of the last time-step is taken into account using an inertness/
constriction parameter, which controls the convergence behaviour of the swarm.
The influence of social and cognitive attraction are weighed using the <i>phi</i> parameters. In the
constriction variant there is a dependence enforced between constriction and the phi, making sure that
the swarm converges slowly but steadily, see the publications of Clerc, e.g. <br>
</body>
</html>

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<html>
<head>
<title>Poly model</title>
</head>
<body>
<EFBFBD>
<h1 align="center">Poly model</h1>
<center>
</center><br>
Please read the JavaEvA manual for a detailed description.
</ul>
</body>
</html>

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<html>
<head>
<title>f_1 : Sphere function</title>
</head>
<body>
<EFBFBD>
<h1 align="center">PolyRBFJama</h1>
<center>
</center><br>
ESPara contains the information describing the Evolution Strategy:
<ul>
<li>The problem to be solved.</li>
<li>A seed value for the random number genarator.</li>
<li>A termination criterium for the algorithm.</li>
<li>The used population.</li>
</ul>
</body>
</html>

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<html>
<head>
<title>RBF model</title>
</head>
<body>
<EFBFBD>
<h1 align="center">RBF model</h1>
<center>
</center><br>
Please read the JavaEvA manual for a detailed description.
</ul>
</body>
</html>

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<html>
<head>
<title>Statistics Parameter Panel</title>
</head>
<body>
<EFBFBD>
<h1 align="center">Statistics Parameter Panel</h1>
<center>
</center><br>
Here you can edit the :
<ul>
<li>Number of statistical independent runs.</li>
<li>Name of result file.</li>
<li>Plot fitness of best, worse or both individuals.</li>
</ul>
</body>
</html>

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<html>
<head>
<title>TRIBES</title>
</head>
<body>
<h1 align="center">TRIBES</h1>
<center>
</center><br>
TRIBES is a parameter-free PSO implementation by Maurice Clerc. It combines several adaptive
mechanisms to achieve good performance in different domains. It uses a dynamic number of particles,
starting usually with 3 and adding new ones during optimization. Therefore, the number of generations
is not directly connected to the number of fitness calls,
because the population may grow (and seldomly shrink).<br>
Also, there are different initialization
methods implemented which are chosen randomly when particles are created. The particles are organized
in loosely connected groups or tribes (therefore the name), creating a kind of small-world topology.
<br>
As TRIBES uses an error approximation to steer the adaptations, a target value should be given, so far in the first
dimension only.
</body>
</html>

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<html>
<head>
<title>RVM model</title>
</head>
<body>
<EFBFBD>
<h1 align="center">RVM model</h1>
<center>
</center><br>
Please read the JavaEvA manual for a detailed description.
</ul>
</body>
</html>