Cleaning up resources (3) Some were moved to the ES/Prob package, some didnt contain real information and were deleted.
This commit is contained in:
Marcel Kronfeld
parent
2221ac4a1d
commit
3e4caef0a7
@ -1,13 +0,0 @@
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<html>
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<head>
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<title>Epsilon SV-Regression</title>
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</head>
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<body>
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<h1 align="center">Epsilon SV-Regression</h1>
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<center>
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</center><br>
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Please read the JavaEvA manual for a detailed description.
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</ul>
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</body>
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</html>
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@ -1,16 +0,0 @@
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<html>
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<head>
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<title>ESIndividual</title>
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</head>
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<body>
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<h1 align="center">ESIndividual</h1>
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<center>
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</center><br>
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This element represents the properties of an individual.
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The most important evolutionary operator of an ES is
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the mutation of the objective variables representing
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the solution of the problem, which is responsible
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for the self-adaptation capability of the ES
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</body>
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</html>
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@ -1,17 +0,0 @@
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<html>
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<head>
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<title>f_1 : Sphere function</title>
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</head>
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<body>
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<h1 align="center">ESInitPopulationSpaceFilling</h1>
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<center>
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</center><br>
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ESPara contains the information describing the Evolution Strategy:
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<ul>
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<li>The problem to be solved.</li>
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<li>A seed value for the random number genarator.</li>
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<li>A termination criterium for the algorithm.</li>
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<li>The used population.</li>
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</ul>
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</body>
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</html>
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@ -1,10 +0,0 @@
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<html>
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<head>
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<title>ESInitPopulationRandom</title>
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</head>
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<body>
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<h1 align="center">ESInitPopulationRandom</h1>
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<center>
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</center><br>
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Here you can specify the number of individuals, which are randomly initialized.
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@ -1,18 +0,0 @@
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<html>
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<head>
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<title>f_1 : Sphere function</title>
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</head>
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<body>
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<h1 align="center">ESInitPopulationSpaceFilling</h1>
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<center>
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</center><br>
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ESPara contains the information describing the Evolution Strategy:
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<ul>
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<li>The problem to be solved.</li>
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<li>A seed value for the random number genarator.</li>
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<li>A termination criterium for the algorithm.</li>
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<li>The used population.</li>
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</ul>
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</body>
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</html>
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@ -1,17 +0,0 @@
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<html>
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<head>
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<title>f_1 : Sphere function</title>
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</head>
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<body>
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<h1 align="center">Parameters for the Evolution Strategy</h1>
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<center>
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</center><br>
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The Java Class ESPara contains the information describing an Evolution Strategy (ES):
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<ul>
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<li>The problem to be solved.</li>
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<li>A seed value for the random number generator.</li>
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<li>A termination criterion for the algorithm.</li>
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<li>The ES population.</li>
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</ul>
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</body>
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</html>
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@ -1,18 +0,0 @@
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<html>
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<head>
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<title>ESPopulation</title>
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</head>
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<body>
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<h1 align="center">ESPopulation</h1>
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<center>
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</center><br>
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ESPopulation contains the information describing an Evolution Strategy (ES):
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<ul>
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<li>A prototype of an individual (contains mutation operator).</li>
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<li>The population size of the parents: lambda.</li>
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<li>The population size of the children: mu.</li>
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<li>A recombination operator.</li>
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<li>A fitness based selection operator.</li>
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</ul>
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</body>
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</html>
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@ -1,18 +0,0 @@
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<html>
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<head>
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<title>ESRecombination</title>
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</head>
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<body>
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<h1 align="center">ESRecombination</h1>
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<center>
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</center><br>
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The recombination operator has the following editable properties:
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<ul>
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<li>strategy for recombination of the strategy parameters of the mutation operators..</li>
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<li>strategy for recombination of the objectives of an individual..</li>
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<li>rho = number of parents, which recombinate to one offspring individual..</li>
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<li>strategy for selecting the input individuals for one recombination.</li>
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</ul>
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</body>
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</html>
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@ -1,12 +0,0 @@
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<html>
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<head>
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<title>ES Comma Selection</title>
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</head>
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<body>
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<h1 align="center">ES Comma Selection Operator</h1>
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<center>
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</center><br>
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The best mu individuals are selected from lambda offspring individuals.
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</body>
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</html>
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@ -1,26 +0,0 @@
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<html>
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<head>
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<title>ES - Median Selection </title>
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</head>
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<body>
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<h1 align="center">ES Median Selection Strategy</h1>
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<center>
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</center><br>
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The main application field of the Median Selection Strategy
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operator are steady state algorithms.
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A standard steady-state ES is equivalent to a (mu + 1) ES.
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Only one individual is generated and evaluated
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at each step and gets immediately integrated into the population.
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Compared to generation based algorithms the information of
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new evaluated individuals can be integrated directly into the optimization process.
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The idea is to approximate the selection mechanism
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of a standard (mu,lambda) ES, by
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using a fitness buffer containing
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fitness values of the last n evaluations.
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Given a relative rate of acceptance r=mu\lambda.
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A newly evaluated individual substitutes the worst individual
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of the population, if it has a better fitness than the r*n best individuals
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in the buffer.
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</body>
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</html>
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<html>
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<head>
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<title>f_1 : Sphere function</title>
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</head>
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<body>
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<h1 align="center">ESSelectionStrategyMedian</h1>
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<center>
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</center><br>
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ESPara contains the information describing the Evolution Strategy:
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<ul>
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<li>The problem to be solved.</li>
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<li>A seed value for the random number genarator.</li>
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<li>A termination criterium for the algorithm.</li>
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<li>The used population.</li>
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</ul>
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</body>
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</html>
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@ -1,13 +0,0 @@
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<html>
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<head>
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<title>Plus Selection Strategy</title>
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</head>
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<body>
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<h1 align="center">ES Plus Selection Operator</h1>
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<center>
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</center><br>
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The best mu individuals are selected from the
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aggregation of the lambda offspring individuals and the mu parents.
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</body>
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</html>
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<html>
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<head>
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<title>Gauss Process Regression Model</title>
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</head>
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<body>
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<h1 align="center">Gauss Process Regression Model</h1>
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<center>
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</center><br>
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Please read the JavaEvA manual for a detailed description.
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</ul>
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</body>
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</html>
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<html>
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<head>
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<title>Model Assisted Evolution Strategy - MAES</title>
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</head>
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<body>
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<h1 align="center">Model Assisted Evolution Strategy - MAES</h1>
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<center>
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</center><br>
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In the pre-selection concept lambdaPlus>lambda
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individuals are generated from mu parents.
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All lambdaPlus individuals are evaluated by a
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surrogate model of the fitness landscape and the estimated
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fitness values are used to pre-select the lambda
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best individuals, which will be evaluated with
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the real fitness function.
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The model is trained at the beginning with a randomly created
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initial population and is updated after each generation
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step with lambda new fitness cases.
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The idea behind this approach is that only the
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most promising individuals with a good fitness prediction
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are evaluated with the true fitness function.
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Every generation a new offspring lambda is evaluated with the real fitness function,
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the model is updated with this information of \lambda fitness cases.
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</body>
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</html>
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<html>
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<head>
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<title>MAESIndividual</title>
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</head>
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<body>
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<h1 align="center">MAESIndividual</h1>
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<center>
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</center><br>
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This element represents the properties of an individual.
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The most important evolutionary operator of an ES is
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the mutation of the objective variables representing
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the solution of the problem, which is responsible
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for the self-adaptation capability of the ES
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</body>
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</html>
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@ -1,17 +0,0 @@
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<html>
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<head>
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<title>MAESPara</title>
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</head>
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<body>
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<h1 align="center">MAESPara</h1>
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<center>
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</center><br>
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MAESPara contains the information describing an Evolution Strategy (ES):
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<ul>
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<li>The problem to be solved.</li>
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<li>A seed value for the random number generator.</li>
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<li>A termination criterion for the algorithm.</li>
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<li>The MAES population.</li>
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</ul>
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</body>
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</html>
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@ -1,23 +0,0 @@
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<html>
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<head>
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<title>MAESPopulation</title>
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</head>
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<body>
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<h1 align="center">Model Assisted Population</h1>
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<center>
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</center><br>
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The MAESPopulation panel contains the information describing the Model Assisted Evolution Strategy (MAES):
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<ul>
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<li>A prototype of an individual (contains mutation operator).</li>
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<li>The size of the model pre-selected individuals: lambdaPlus>=lambda.
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For lambdaPlus=lambda you have no model impact.</li>
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<li>The regression model for fitness prediction.</li>
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<li>The model size is given by the number of last evaluated individuals,
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which are used to train the model.</li>
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<li>The population size of the parents: lambda.</li>
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<li>The population size of the children: mu.</li>
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<li>A recombination operator.</li>
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<li>A fitness based selection operator.</li>
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</ul>
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</body>
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</html>
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@ -1,18 +0,0 @@
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<html>
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<head>
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<title>MAESRecombination</title>
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</head>
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<body>
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<h1 align="center">MAESRecombination</h1>
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<center>
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</center><br>
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The recombination operator has the following editable properties:
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<ul>
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<li>strategy for recombination of the strategy parameters of the mutation operators..</li>
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<li>strategy for recombination of the objectives of an individual..</li>
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<li>rho = number of parents, which recombinate to one offspring individual..</li>
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<li>strategy for selecting the input individuals for one recombination.</li>
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</ul>
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</body>
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</html>
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@ -1,12 +0,0 @@
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<html>
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<head>
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<title>MAES Comma Selection</title>
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</head>
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<body>
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<h1 align="center">MAES Comma Selection Operator</h1>
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<center>
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</center><br>
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The best mu individuals are selected from lambda offspring individuals.
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</body>
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</html>
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@ -1,32 +0,0 @@
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<html>
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<head>
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<title>MAES - Median Selection </title>
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</head>
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<body>
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<h1 align="center">MAES Median Selection Strategy</h1>
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<center>
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</center><br>
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The main application field of the Median Selection Strategy
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operator are steady state algorithms.
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A standard steady-state ES is equivalent to a (mu + 1) ES.
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Only one individual is generated and evaluated
|
||||
at each step and gets immediately integrated into the population.
|
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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
|
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using a fitness buffer containing
|
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fitness values of the last n evaluations.
|
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Given a relative rate of acceptance r=mu\lambda.
|
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A newly evaluated individual substitutes the worst individual
|
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of the population, if it has a better fitness than the r*n best individuals
|
||||
in the buffer.
|
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<ul>
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<li>The problem to be solved.</li>
|
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<li>A seed value for the random number genarator.</li>
|
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<li>A termination criterium for the algorithm.</li>
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<li>The used population.</li>
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</ul>
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</body>
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</html>
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@ -1,13 +0,0 @@
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<html>
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<head>
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<title>Plus Selection Strategy</title>
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</head>
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<body>
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<h1 align="center">MAES Plus Selection Operator</h1>
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<center>
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</center><br>
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The best mu individuals are selected from the
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aggredation of the lambda offspring individuals and the mu parents.
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</body>
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</html>
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@ -1,13 +0,0 @@
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<html>
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<head>
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<title>CMA Mutation</title>
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</head>
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<body>
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<h1 align="center">CMA Mutation</h1>
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<center>
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</center><br>
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Please read the JavaEvA manual for a detailed description.
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</ul>
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</body>
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</html>
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@ -1,13 +0,0 @@
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<html>
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<head>
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<title>f_1 : Sphere function</title>
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</head>
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<body>
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<h1 align="center">MutationMSRGlobal</h1>
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<center>
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</center><br>
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Please read the JavaEvA manual for a detailed description.
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</ul>
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</body>
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</html>
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<html>
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<head>
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<title>f_1 : Sphere function</title>
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</head>
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<body>
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<h1 align="center">MutationMSRSeperate</h1>
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<center>
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</center><br>
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<center>
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</center><br>
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Please read the JavaEvA manual for a detailed description.
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</ul>
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</body>
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</html>
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<html>
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<head>
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<title>MVA Mutation</title>
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</head>
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<body>
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<h1 align="center">MVA Mutation</h1>
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<center>
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</center><br>
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Please read the JavaEvA manual for a detailed description.
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</ul>
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</body>
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</html>
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@ -1,13 +0,0 @@
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<html>
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<head>
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<title>Random Mutation</title>
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</head>
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<body>
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<h1 align="center">Random Mutation</h1>
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<center>
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</center><br>
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Please read the JavaEvA manual for a detailed description.
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</ul>
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</body>
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</html>
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@ -1,13 +0,0 @@
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<html>
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<head>
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<title>Success Rule Mutation</title>
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</head>
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<body>
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<h1 align="center">Success Rule</h1>
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<center>
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</center><br>
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Please read the JavaEvA manual for a detailed description.
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</ul>
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</body>
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</html>
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@ -1,13 +0,0 @@
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<html>
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<head>
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<title>NU SV-Regression</title>
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</head>
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<body>
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<h1 align="center">NU SV-Regression</h1>
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<center>
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</center><br>
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Please read the JavaEvA manual for a detailed description.
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</ul>
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</body>
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</html>
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@ -1,13 +0,0 @@
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<html>
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<head>
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<title>Poly model</title>
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</head>
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<body>
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<h1 align="center">Poly model</h1>
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<center>
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</center><br>
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Please read the JavaEvA manual for a detailed description.
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</ul>
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</body>
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</html>
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@ -1,18 +0,0 @@
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<html>
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<head>
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<title>f_1 : Sphere function</title>
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</head>
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<body>
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<h1 align="center">PolyRBFJama</h1>
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<center>
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</center><br>
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ESPara contains the information describing the Evolution Strategy:
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<ul>
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<li>The problem to be solved.</li>
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<li>A seed value for the random number genarator.</li>
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<li>A termination criterium for the algorithm.</li>
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<li>The used population.</li>
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</ul>
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||||
</body>
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||||
</html>
|
||||
@ -1,13 +0,0 @@
|
||||
<html>
|
||||
<head>
|
||||
<title>RBF model</title>
|
||||
</head>
|
||||
<body>
|
||||
|
||||
<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>RVM model</title>
|
||||
</head>
|
||||
<body>
|
||||
|
||||
<h1 align="center">RVM model</h1>
|
||||
<center>
|
||||
</center><br>
|
||||
Please read the JavaEvA manual for a detailed description.
|
||||
</ul>
|
||||
</body>
|
||||
</html>
|
||||
Loading…
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Reference in New Issue
Block a user