Cleaning up resources (3) Some were moved to the ES/Prob package, some didnt contain real information and were deleted.

2008-04-01 09:40:48 +00:00 · 2008-04-01 09:40:48 +00:00 · 3e4caef0a7
commit 3e4caef0a7
parent 2221ac4a1d
34 changed files with 0 additions and 517 deletions
--- a/resources/EPSILON_SVM.html
+++ b/resources/EPSILON_SVM.html
@ -1,13 +0,0 @@
 <html>
 <head>
 <title>Epsilon SV-Regression</title>
 </head>
 <body>
 <h1 align="center">Epsilon SV-Regression</h1>
 <center>
 </center><br>
 Please read the JavaEvA manual for a detailed description.
 </ul>
 </body>
 </html>
--- a/resources/ESIndividual.html
+++ b/resources/ESIndividual.html
@ -1,16 +0,0 @@
 <html>
 <head>
 <title>ESIndividual</title>
 </head>
 <body>
 <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>
--- a/resources/ESInitPopulationDOptimal.html
+++ b/resources/ESInitPopulationDOptimal.html
@ -1,17 +0,0 @@
 <html>
 <head>
 <title>f_1 : Sphere function</title>
 </head>
 <body>
 <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> 
--- a/resources/ESInitPopulationRandom.html
+++ b/resources/ESInitPopulationRandom.html
@ -1,10 +0,0 @@
 <html>
 <head>
 <title>ESInitPopulationRandom</title>
 </head>
 <body>
 <h1 align="center">ESInitPopulationRandom</h1>
 <center>
 </center><br>
 Here you can specify the number of individuals, which are randomly initialized.
--- a/resources/ESInitPopulationSpaceFilling.html
+++ b/resources/ESInitPopulationSpaceFilling.html
@ -1,18 +0,0 @@
 <html>
 <head>
 <title>f_1 : Sphere function</title>
 </head>
 <body>
 <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> 
--- a/resources/ESPara.html
+++ b/resources/ESPara.html
@ -1,17 +0,0 @@
 <html>
 <head>
 <title>f_1 : Sphere function</title>
 </head>
 <body> 
 <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>
--- a/resources/ESPopulation.html
+++ b/resources/ESPopulation.html
@ -1,18 +0,0 @@
 <html>
 <head>
 <title>ESPopulation</title>
 </head>
 <body> 
 <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>
--- a/resources/ESRecombination.html
+++ b/resources/ESRecombination.html
@ -1,18 +0,0 @@
 <html>
 <head>
 <title>ESRecombination</title>
 </head>
 <body>
 <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>
--- a/resources/ESSelectionStrategyComma.html
+++ b/resources/ESSelectionStrategyComma.html
@ -1,12 +0,0 @@
 <html>
 <head>
 <title>ES Comma Selection</title>
 </head>
 <body>
 <h1 align="center">ES Comma Selection Operator</h1>
 <center>
 </center><br>
 The best mu individuals are selected from lambda offspring individuals.
 </body>
 </html>
--- a/resources/ESSelectionStrategyMedian.html
+++ b/resources/ESSelectionStrategyMedian.html
@ -1,26 +0,0 @@
 <html>
 <head>
 <title>ES - Median Selection </title>
 </head>
 <body>
 <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>
--- a/resources/ESSelectionStrategyMixed.html
+++ b/resources/ESSelectionStrategyMixed.html
@ -1,18 +0,0 @@
 <html>
 <head>
 <title>f_1 : Sphere function</title>
 </head>
 <body>
 <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> 
--- a/resources/ESSelectionStrategyPlus.html
+++ b/resources/ESSelectionStrategyPlus.html
@ -1,13 +0,0 @@
 <html>
 <head>
 <title>Plus Selection Strategy</title>
 </head>
 <body>
 <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>
--- a/resources/GaussProcess.html
+++ b/resources/GaussProcess.html
@ -1,12 +0,0 @@
 <html>
 <head>
 <title>Gauss Process Regression Model</title>
 </head>
 <body>
 <h1 align="center">Gauss Process Regression Model</h1>
 <center>
 </center><br>
 Please read the JavaEvA manual for a detailed description.
 </ul>
 </body>
 </html>
--- a/resources/MAES.html
+++ b/resources/MAES.html
@ -1,28 +0,0 @@
 <html>
 <head>
 <title>Model Assisted Evolution Strategy - MAES</title>
 </head>
 <body>
 <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>
--- a/resources/MAESIndividual.html
+++ b/resources/MAESIndividual.html
@ -1,16 +0,0 @@
 <html>
 <head>
 <title>MAESIndividual</title>
 </head>
 <body>
 <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>
--- a/resources/MAESPara.html
+++ b/resources/MAESPara.html
@ -1,17 +0,0 @@
 <html>
 <head>
 <title>MAESPara</title>
 </head>
 <body> 
 <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>
--- a/resources/MAESPopulation.html
+++ b/resources/MAESPopulation.html
@ -1,23 +0,0 @@
 <html>
 <head>
 <title>MAESPopulation</title>
 </head>
 <body> 
 <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>
--- a/resources/MAESRecombination.html
+++ b/resources/MAESRecombination.html
@ -1,18 +0,0 @@
 <html>
 <head>
 <title>MAESRecombination</title>
 </head>
 <body>
 <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>
--- a/resources/MAESSelectionStrategyComma.html
+++ b/resources/MAESSelectionStrategyComma.html
@ -1,12 +0,0 @@
 <html>
 <head>
 <title>MAES Comma Selection</title>
 </head>
 <body>
 <h1 align="center">MAES Comma Selection Operator</h1>
 <center>
 </center><br>
 The best mu individuals are selected from lambda offspring individuals.
 </body>
 </html>
--- a/resources/MAESSelectionStrategyMedian.html
+++ b/resources/MAESSelectionStrategyMedian.html
@ -1,32 +0,0 @@
 <html>
 <head>
 <title>MAES - Median Selection </title>
 </head>
 <body>
 <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>
--- a/resources/MAESSelectionStrategyPlus.html
+++ b/resources/MAESSelectionStrategyPlus.html
@ -1,13 +0,0 @@
 <html>
 <head>
 <title>Plus Selection Strategy</title>
 </head>
 <body>
 <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>
--- a/resources/MutationCMA.html
+++ b/resources/MutationCMA.html
@ -1,13 +0,0 @@
 <html>
 <head>
 <title>CMA Mutation</title>
 </head>
 <body>
 <h1 align="center">CMA Mutation</h1>
 <center>
 </center><br>
 Please read the JavaEvA manual for a detailed description.
 </ul>
 </body>
 </html>
--- a/resources/MutationMSRGlobal.html
+++ b/resources/MutationMSRGlobal.html
@ -1,13 +0,0 @@
 <html>
 <head>
 <title>f_1 : Sphere function</title>
 </head>
 <body>
 <h1 align="center">MutationMSRGlobal</h1>
 <center>
 </center><br>
 Please read the JavaEvA manual for a detailed description.
 </ul>
 </body>
 </html>
--- a/resources/MutationMSRSeperate.html
+++ b/resources/MutationMSRSeperate.html
@ -1,15 +0,0 @@
 <html>
 <head>
 <title>f_1 : Sphere function</title>
 </head>
 <body>
 <h1 align="center">MutationMSRSeperate</h1>
 <center>
 </center><br>
 <center>
 </center><br>
 Please read the JavaEvA manual for a detailed description.
 </ul>
 </body>
 </html>
--- a/resources/MutationMVA.html
+++ b/resources/MutationMVA.html
@ -1,13 +0,0 @@
 <html>
 <head>
 <title>MVA Mutation</title>
 </head>
 <body>
 <h1 align="center">MVA Mutation</h1>
 <center>
 </center><br>
 Please read the JavaEvA manual for a detailed description.
 </ul>
 </body>
 </html>
--- a/resources/MutationRandom.html
+++ b/resources/MutationRandom.html
@ -1,13 +0,0 @@
 <html>
 <head>
 <title>Random Mutation</title>
 </head>
 <body>
 <h1 align="center">Random Mutation</h1>
 <center>
 </center><br>
 Please read the JavaEvA manual for a detailed description.
 </ul>
 </body>
 </html>
--- a/resources/MutationSuccessRule.html
+++ b/resources/MutationSuccessRule.html
@ -1,13 +0,0 @@
 <html>
 <head>
 <title>Success Rule Mutation</title>
 </head>
 <body>
 <h1 align="center">Success Rule</h1>
 <center>
 </center><br>
 Please read the JavaEvA manual for a detailed description.
 </ul>
 </body>
 </html>
--- a/resources/NU_SVM.html
+++ b/resources/NU_SVM.html
@ -1,13 +0,0 @@
 <html>
 <head>
 <title>NU SV-Regression</title>
 </head>
 <body>
 <h1 align="center">NU SV-Regression</h1>
 <center>
 </center><br>
 Please read the JavaEvA manual for a detailed description.
 </ul>
 </body>
 </html>
--- a/resources/Poly.html
+++ b/resources/Poly.html
@ -1,13 +0,0 @@
 <html>
 <head>
 <title>Poly model</title>
 </head>
 <body>
 <h1 align="center">Poly model</h1>
 <center>
 </center><br>
 Please read the JavaEvA manual for a detailed description.
 </ul>
 </body>
 </html>
--- a/resources/PolyRBFJama.html
+++ b/resources/PolyRBFJama.html
@ -1,18 +0,0 @@
 <html>
 <head>
 <title>f_1 : Sphere function</title>
 </head>
 <body>
 <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> 
--- a/resources/RBF.html
+++ b/resources/RBF.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>
--- a/resources/images/QueenB.gif
+++ b/resources/images/QueenB.gif
--- a/resources/images/QueenW.gif
+++ b/resources/images/QueenW.gif
--- a/resources/rvm.html
+++ b/resources/rvm.html
@ -1,13 +0,0 @@
 <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>