OpenDino - Benutzerbeiträge [de]

Documentation/Modules

2019-04-24T21:04:16Z

Admin:

'''Modules''' contain all the functionality for optimizing and learning. One '''Modules''' may contain an optimization algorithm, an artificial neural network, or a problem to optimize.

Here is a list of documented modules in OpenDino. Further modules may exist, but may not yet be documented.

== Single Objective Optimization Algorithms ==

=== Indirect, Deterministic Algorithms ===
Indirect algorithms use gradient or higher order derivative information in the optimization.

Not implemented, yet.

=== Direct, Deterministic Algorithms ===
These algorithms neither use gradient information nor stochastic processes.

* [[Documentation/Modules/OptAlgSimplex | <code>OptAlgSimplex</code>]]: Nelder-Mead Simplex Algorithm

=== Indirect, Stochastic Algorithms ===

These algorithms do not use gradient information but require stochastic processes (i.e. random numbers) in their search.

* Evolutionary Algorithms
** [[Documentation/Modules/OptAlgOpO|<code>OptAlgOpO</code>]]: 1+1 Evolution Strategy with 1/5 Success Rule: the (1+1)-ES
** [[Documentation/Modules/OptAlgCMA|<code>OptAlgCMA</code>]]: A Multi-member Evolution Strategy with Covariance Matrix Adaptation: the CMA-ES

== Single and Multi-Objective Optimization Algorithms ==

=== Indirect, Stochastic Algorithms ===
* Evolutionary Algorithms
** [[Documentation/Modules/OptAlgMoCMA | <code>OptAlgMoCMA</code>]]: Elitist Evolution Strategy with Covariance Matrix Adaptation
* Particle Methods
** [[Documentation/Modules/OptAlgMOPSO | <code>OptAlgMOPSO</code>]]: Particle Swarm Optimization Algorithm

== Design of Experiments ==
* [[Documentation/Modules/DoePlanner | <code>DoePlanner</code>]]: A Module Containing Different DoE plans
* [[Documentation/Modules/RandomSampling | <code>RandomSampling</code>]]: Uniform Random Sampling (Monte Carlo)

== Optimization in General ==
* [[Documentation/Modules/BoundHandler | <code>BoundHandler</code>]]: For Optimization Algorithms without Bound Handling
* [[Documentation/Modules/ConstraintHandler | <code>ConstraintHandler </code>]]: For Optimization Algorithms without Constraint Handling

== Optimization Problems ==
* [[Documentation/Modules/ProblemSimple | <code>ProblemSimple</code>]] A Simple Test Problem
* [[Documentation/Modules/ContinuousTestProblems | <code>ContinuousTestProblems </code>]]: A Set of Single Objective Test Problem
* [[Documentation/Modules/ContinuousMOTestProblems | <code>ContinuousMOTestProblems </code>]]: A Set of Multi-Objective Test Problem

* [[Documentation/Modules/ProblemTruss | <code>ProblemTruss </code>]]: The goal is to opzimize the thickness of 10 trusses. The weight of the truess, maximum stress, and displacement can each be set either as objective or constraint.

== Machine Learning ==
* [[Documentation/Modules/NeuralNetwork | <code>NeuralNetwork</code>]]: Artificial Neural Network

== Miscellaneous Modules ==
* [[Documentation/Modules/SurrogateManager | <code>SurrogateManager</code>]]: A Framework for Surrogate Managing

Documentation/Modules/OptAlgSimplex

2019-04-24T21:03:33Z

Admin: Admin verschob die Seite Documentation/Modules/OptAlgSIMPLEX nach Documentation/Modules/OptAlgSimplex

==Summary==

This optimization module is an implementation of the popular Nelder-Mead Simplex Algorithm (1965) [[#NelMead | (1)]]. The SIMPLEX method is a standard direct, deterministic optimization algorithm, which is implemented in many numerical tools (e.g. as method <code>fminsearch.m</code> in the MATLAB(R) optimization toolbox or in [http://www.gnu.org/software/octave/ GNU Octave]). A good introduction is given by Wright [[#Wright | (2)]].

The algorithm works best for a small number of design variables (1-10, sometimes 1-20). It fails, for example, on the Rosenbrock function, if more than 10 design variables are given.

==Properties==

===General===

{| class="wikitable" style="text-align:left" border="2" cellpadding="5" cellspacing="5"
|-
! Algorithm
| deterministic
|-
! Design Variables
| Written for continuous variables. Discrete or mixed variables are NOT possible.
|-
! Objectives
| single-objective for minimization.
|-
! Constraint handling
| no
|-
! Boundary handling
| no
|-
! Initialization
| Requires at least one of the following: initial solution, initial search region, or bounds. |-
|}

===Connections===

{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Starting at this module
| Module requires exactly one connection of type <code>optimization</code>.
|-
! Ending at this module
|-
|}

===Actions===

{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Name !! Description
|-
! '''Run'''
| starts the optimization.
|-
|}

===Options===

The options are currently described as "pop-up help".

==Module Description==

The SIMPLEX algorithm is implemented using the paper of Wright [[#Wright | (2)]].
However, some extensions are made as e.g. the initialization is not given in the references.

=== Initialization ===

First, an initial simplex is generated. Here, a [http://en.wikipedia.org/wiki/Simplex simplex] is a body in the N-dimensional design space that has N+1 vertices. The vertices are connected by straight lines. For two (N=2) and three (N=3) design variables, a simplex is a triangle or tetrahedron, respectively.

If the optimization problem provides an initial solution, this point is taken as first vertex of the simplex. If not, the initial simplex gets a random position obeying certain rules.

We distinguish several cases, depending on the problem properties (<code>initial solution, initial search region, bounds</code>).

{| border="1" cellspacing="0"
! colspan="3" | Problem Properties Given? || Action
|-
| <code>initial solution</code> || <code>initial search region</code> || <code>bounds</code> ||
|-
|Yes || No || No || option <code>Initial Size of the Simplex</code> defines the absolute length of the simplex in each design variable direction. I.e. a simplex is constructed, such that the distance between each vertex and the plane generated by the remaining points has this length.
|-
|Yes or No || Yes || Yes or No || option <code>Initial Size of the Simplex</code> defines the relative length of the simplex in each design variable direction compared to the size of the initial search region (i.e. difference upper to lower limit). Bounds are ignored.
|-
|Yes or No || No || Yes || option <code>Initial Size of the Simplex</code> defines the relative length of the simplex in each design variable direction compared to the size of the bounds (i.e. difference upper to lower limit).
|}

=== Optimization ===

The SIMPLEX method follows a deterministic procedure. Except the initialization, always one new point is generated and evaluated. Thus, parallelization is not implemented.

==Usage==

... todo

==Source Code==

ToDo:Link to SVN

==References==

1 Nelder, J. A. and Mead, R. "A Simplex Method for Function Minimization." Comput. J. 7, 308-313, 1965.

2 M. H. Wright. "Direct search methods: once scorned, now respectable". In D. F. Griffiths and G. A. Watson, editors, Numerical Analysis 1995 (Proceedings of the 1995 Dundee Biennial Conference in Numerical Analysis),
pages 191–208. Addison Wesley Longman, 1996.

Documentation/Modules/OptAlgSIMPLEX

2019-04-24T21:03:33Z

Admin: Admin verschob die Seite Documentation/Modules/OptAlgSIMPLEX nach Documentation/Modules/OptAlgSimplex

#WEITERLEITUNG [[Documentation/Modules/OptAlgSimplex]]

Documentation/Modules/ContinuousMOTestProblems

2019-04-07T16:29:40Z

Admin: /* Module Description */

==Summary==

The module <code>ContinuousMOTestProblems</code> contains a set of multi-objective optimization problems, which can be used to test optimization algorithms.

==Properties==

===General===

{| class="wikitable" style="text-align:left" border="2" cellpadding="5" cellspacing="5"
|-
! Algorithm
| deterministic test function
|-
! Design Variables
| continuous design variables only
|-
! Objectives
| multi-objective for minimization
|-
! Constraints
| none
|-
! Boundaries
| problem dependent
|-
! Initial Search Region
| problem dependent
|-
! Typical X
| not set
|-
|}

===Connections===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Starting at his module
| none
|-
! Ending at this module
| Connections of type <code>optimization</code>
|-
|}

===Actions===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Name !! Description
|-
! -
| -
|-
|}

===Options===
The options are the number of discrete and continuous variables and an optional time delay for per evaluation.

==Module Description==

=== Multi-Objective Sphere ===

The multi-objective sphere function '''f''' is a set of quadratic functions. More precisely, each ''fi'' is computed as the square of all continuous variables.

''fi''('''x''') = (''xi'' - 1)2 + sum''j, j~=i'' (x''j''2)

For example

''f1''('''x''') = (''x1'' - 1)2 + ''x''22 + ''x''32 + ...

''f2''('''x''') = ''x1''2 + (''x2'' - 1)2 + ''x''32 + ...

...

The Pareto front for the two-objective case is given in the following picture:

{|
| [[Image:moo_sphere_ds.png|100x100px|center|thumb|300x300px|Design space]]
| [[Image:moo_sphere.png|100x100px|center|thumb|300x300px|Objective space]]
|}

==Usage==
-
==Source Code==

https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/modules/problems/ContinuousMOTestProblems.java

==References==

-

Datei:Moo sphere ds.png

2019-04-07T16:27:25Z

Admin:

Documentation/Modules/ContinuousMOTestProblems

2019-04-07T16:20:07Z

Admin: /* Multi-Objective Sphere */

==Summary==

The module <code>ContinuousMOTestProblems</code> contains a set of multi-objective optimization problems, which can be used to test optimization algorithms.

==Properties==

===General===

{| class="wikitable" style="text-align:left" border="2" cellpadding="5" cellspacing="5"
|-
! Algorithm
| deterministic test function
|-
! Design Variables
| continuous design variables only
|-
! Objectives
| multi-objective for minimization
|-
! Constraints
| none
|-
! Boundaries
| problem dependent
|-
! Initial Search Region
| problem dependent
|-
! Typical X
| not set
|-
|}

===Connections===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Starting at his module
| none
|-
! Ending at this module
| Connections of type <code>optimization</code>
|-
|}

===Actions===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Name !! Description
|-
! -
| -
|-
|}

===Options===
The options are the number of discrete and continuous variables and an optional time delay for per evaluation.

==Module Description==

=== Multi-Objective Sphere ===

The multi-objective sphere function '''f''' is a set of quadratic functions. More precisely, each ''fi'' is computed as the square of all continuous variables.

''fi''('''x''') = (''xi'' - 1)2 + sum''j, j~=i'' (x''j''2)

For example

''f1''('''x''') = (''x1'' - 1)2 + ''x''22 + ''x''32 + ...

''f2''('''x''') = ''x1''2 + (''x2'' - 1)2 + ''x''32 + ...

...

The Pareto front for the two-objective case is given in the following picture:

[[Image:moo_sphere.png|100x100px|center|thumb|300x300px|Pareto front of the multi-objective sphere function]]

==Usage==
-
==Source Code==

https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/modules/problems/ContinuousMOTestProblems.java

==References==

-

Datei:Moo sphere.png

2019-04-07T16:13:08Z

Admin: Admin lud eine neue Version von Datei:Moo sphere.png hoch

Documentation/Modules/ContinuousMOTestProblems

2019-04-07T13:53:58Z

Admin: /* Multi-Objective Sphere */

==Summary==

The module <code>ContinuousMOTestProblems</code> contains a set of multi-objective optimization problems, which can be used to test optimization algorithms.

==Properties==

===General===

{| class="wikitable" style="text-align:left" border="2" cellpadding="5" cellspacing="5"
|-
! Algorithm
| deterministic test function
|-
! Design Variables
| continuous design variables only
|-
! Objectives
| multi-objective for minimization
|-
! Constraints
| none
|-
! Boundaries
| problem dependent
|-
! Initial Search Region
| problem dependent
|-
! Typical X
| not set
|-
|}

===Connections===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Starting at his module
| none
|-
! Ending at this module
| Connections of type <code>optimization</code>
|-
|}

===Actions===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Name !! Description
|-
! -
| -
|-
|}

===Options===
The options are the number of discrete and continuous variables and an optional time delay for per evaluation.

==Module Description==

=== Multi-Objective Sphere ===

The multi-objective sphere function '''f''' is a set of quadratic functions. More precisely, each ''fi'' is computed as the square of all continuous variables.

''fi''('''x''') = (''xi'' - 1)2 + sum''j, j~=i'' (x''j''2)

For example

''f1''('''x''') = (''x1'' - 1)2 + ''x''22 + ''x''32 + ...

''f2''('''x''') = ''x1''2 + (''x2'' - 1)2 + ''x''32 + ...

...

The Pareto front for the two-objective case is given in the following picture:

[[Image:moo_sphere.png|frame|center|10px|Pareto front of the multi-objective sphere function]]

==Usage==
-
==Source Code==

https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/modules/problems/ContinuousMOTestProblems.java

==References==

-

Datei:Moo sphere.png

2019-04-07T13:49:03Z

Admin:

Documentation/Modules/ContinuousMOTestProblems

2019-04-07T13:31:13Z

Admin: /* Multi-Objective Sphere */

==Summary==

The module <code>ContinuousMOTestProblems</code> contains a set of multi-objective optimization problems, which can be used to test optimization algorithms.

==Properties==

===General===

{| class="wikitable" style="text-align:left" border="2" cellpadding="5" cellspacing="5"
|-
! Algorithm
| deterministic test function
|-
! Design Variables
| continuous design variables only
|-
! Objectives
| multi-objective for minimization
|-
! Constraints
| none
|-
! Boundaries
| problem dependent
|-
! Initial Search Region
| problem dependent
|-
! Typical X
| not set
|-
|}

===Connections===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Starting at his module
| none
|-
! Ending at this module
| Connections of type <code>optimization</code>
|-
|}

===Actions===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Name !! Description
|-
! -
| -
|-
|}

===Options===
The options are the number of discrete and continuous variables and an optional time delay for per evaluation.

==Module Description==

=== Multi-Objective Sphere ===

The multi-objective sphere function '''f''' is a set of quadratic functions. More precisely, each ''fi'' is computed as the square of all continuous variables.

''fi''('''x''') = (''xi'' - 1)2 + sum''j, j~=i'' (x''j''2)

For example

''f1''('''x''') = (''x1'' - 1)2 + ''x''22 + ''x''32 + ...

''f2''('''x''') = ''x1''2 + (''x2'' - 1)2 + ''x''32 + ...

...

The Pareto front for the two-objective case is given in the following picture:

==Usage==
-
==Source Code==

https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/modules/problems/ContinuousMOTestProblems.java

==References==

-

Documentation/Modules/ContinuousMOTestProblems

2019-04-07T13:19:00Z

Admin: /* Multi-Objective Sphere */

==Summary==

The module <code>ContinuousMOTestProblems</code> contains a set of multi-objective optimization problems, which can be used to test optimization algorithms.

==Properties==

===General===

{| class="wikitable" style="text-align:left" border="2" cellpadding="5" cellspacing="5"
|-
! Algorithm
| deterministic test function
|-
! Design Variables
| continuous design variables only
|-
! Objectives
| multi-objective for minimization
|-
! Constraints
| none
|-
! Boundaries
| problem dependent
|-
! Initial Search Region
| problem dependent
|-
! Typical X
| not set
|-
|}

===Connections===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Starting at his module
| none
|-
! Ending at this module
| Connections of type <code>optimization</code>
|-
|}

===Actions===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Name !! Description
|-
! -
| -
|-
|}

===Options===
The options are the number of discrete and continuous variables and an optional time delay for per evaluation.

==Module Description==

=== Multi-Objective Sphere ===

The multi-objective sphere function '''f''' is a set of quadratic functions. More precisely, each ''fi'' is computed as the square of all continuous variables.

''fi''('''x''') = (''xi'' - 1)2 + sum''j, j~=i'' (x''j''2)

For example
''f1''('''x''') = (''x1'' - 1)2 + ''x''22 + ''x''32 + ...
''f2''('''x''') = ''x1'' 2 + (''x2'' - 1)2 + ''x''32 + ...
...

==Usage==
-
==Source Code==

https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/modules/problems/ContinuousMOTestProblems.java

==References==

-

Documentation/Modules/ContinuousMOTestProblems

2019-04-07T13:14:30Z

Admin: /* Multi-Objective Sphere */

==Summary==

The module <code>ContinuousMOTestProblems</code> contains a set of multi-objective optimization problems, which can be used to test optimization algorithms.

==Properties==

===General===

{| class="wikitable" style="text-align:left" border="2" cellpadding="5" cellspacing="5"
|-
! Algorithm
| deterministic test function
|-
! Design Variables
| continuous design variables only
|-
! Objectives
| multi-objective for minimization
|-
! Constraints
| none
|-
! Boundaries
| problem dependent
|-
! Initial Search Region
| problem dependent
|-
! Typical X
| not set
|-
|}

===Connections===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Starting at his module
| none
|-
! Ending at this module
| Connections of type <code>optimization</code>
|-
|}

===Actions===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Name !! Description
|-
! -
| -
|-
|}

===Options===
The options are the number of discrete and continuous variables and an optional time delay for per evaluation.

==Module Description==

=== Multi-Objective Sphere ===

The multi-objective sphere function '''f''' is a set of quadratic functions. More precisely, each ''fi'' is computed as the square of all continuous variables.

''fi''('''x''') = ''xi''2 + sum''j, j~=i'' (x''j''2)

This function is also referred to as the ''sphere function''.

==Usage==
-
==Source Code==

https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/modules/problems/ContinuousMOTestProblems.java

==References==

-

Documentation/Modules/ContinuousMOTestProblems

2019-04-07T13:10:33Z

Admin: /* Computation of the Objective */

==Summary==

The module <code>ContinuousMOTestProblems</code> contains a set of multi-objective optimization problems, which can be used to test optimization algorithms.

==Properties==

===General===

{| class="wikitable" style="text-align:left" border="2" cellpadding="5" cellspacing="5"
|-
! Algorithm
| deterministic test function
|-
! Design Variables
| continuous design variables only
|-
! Objectives
| multi-objective for minimization
|-
! Constraints
| none
|-
! Boundaries
| problem dependent
|-
! Initial Search Region
| problem dependent
|-
! Typical X
| not set
|-
|}

===Connections===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Starting at his module
| none
|-
! Ending at this module
| Connections of type <code>optimization</code>
|-
|}

===Actions===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Name !! Description
|-
! -
| -
|-
|}

===Options===
The options are the number of discrete and continuous variables and an optional time delay for per evaluation.

==Module Description==

=== Multi-Objective Sphere ===

The multi-objective sphere function '''f''' is a set of quadratic functions. More precisely, each ''fi'' is computed as the square of all continuous variables.

''fi''('''x''') = sumi(xi2)

This function is also referred to as the ''sphere function''.

==Usage==
-
==Source Code==

https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/modules/problems/ContinuousMOTestProblems.java

==References==

-

Documentation/Modules/ContinuousMOTestProblems

2019-04-07T13:08:40Z

Admin: /* General */

==Summary==

The module <code>ContinuousMOTestProblems</code> contains a set of multi-objective optimization problems, which can be used to test optimization algorithms.

==Properties==

===General===

{| class="wikitable" style="text-align:left" border="2" cellpadding="5" cellspacing="5"
|-
! Algorithm
| deterministic test function
|-
! Design Variables
| continuous design variables only
|-
! Objectives
| multi-objective for minimization
|-
! Constraints
| none
|-
! Boundaries
| problem dependent
|-
! Initial Search Region
| problem dependent
|-
! Typical X
| not set
|-
|}

===Connections===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Starting at his module
| none
|-
! Ending at this module
| Connections of type <code>optimization</code>
|-
|}

===Actions===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Name !! Description
|-
! -
| -
|-
|}

===Options===
The options are the number of discrete and continuous variables and an optional time delay for per evaluation.

==Module Description==

=== Computation of the Objective ===

The single objective function ''f'' is a quadratic function. More precisely, ''f'' is computed as the square of all continuous and discrete variables.

''f''('''x''') = sumi(xi2)

This function is also referred to as the ''sphere function''.

==Usage==
-
==Source Code==

https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/modules/problems/ContinuousMOTestProblems.java

==References==

-

Documentation/Modules/ContinuousMOTestProblems

2019-04-07T13:08:17Z

Admin: /* Summary */

==Summary==

The module <code>ContinuousMOTestProblems</code> contains a set of multi-objective optimization problems, which can be used to test optimization algorithms.

==Properties==

===General===

{| class="wikitable" style="text-align:left" border="2" cellpadding="5" cellspacing="5"
|-
! Algorithm
| deterministic test function
|-
! Design Variables
| Continuous design variables only
|-
! Objectives
| multi-objective for minimization
|-
! Constraints
| none
|-
! Boundaries
| problem dependent
|-
! Initial Search Region
| problem dependent
|-
! Typical X
| not set.
|-
|}

===Connections===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Starting at his module
| none
|-
! Ending at this module
| Connections of type <code>optimization</code>
|-
|}

===Actions===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Name !! Description
|-
! -
| -
|-
|}

===Options===
The options are the number of discrete and continuous variables and an optional time delay for per evaluation.

==Module Description==

=== Computation of the Objective ===

The single objective function ''f'' is a quadratic function. More precisely, ''f'' is computed as the square of all continuous and discrete variables.

''f''('''x''') = sumi(xi2)

This function is also referred to as the ''sphere function''.

==Usage==
-
==Source Code==

https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/modules/problems/ContinuousMOTestProblems.java

==References==

-

Documentation/Modules/ContinuousMOTestProblems

2019-04-07T13:07:52Z

Admin: /* Source Code */

==Summary==

The module <code>ContinuousMOTestProblems</code> contains a set of multi-objective optimization problesm, which can be used to test optimization algorithms.

==Properties==

===General===

{| class="wikitable" style="text-align:left" border="2" cellpadding="5" cellspacing="5"
|-
! Algorithm
| deterministic test function
|-
! Design Variables
| Continuous design variables only
|-
! Objectives
| multi-objective for minimization
|-
! Constraints
| none
|-
! Boundaries
| problem dependent
|-
! Initial Search Region
| problem dependent
|-
! Typical X
| not set.
|-
|}

===Connections===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Starting at his module
| none
|-
! Ending at this module
| Connections of type <code>optimization</code>
|-
|}

===Actions===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Name !! Description
|-
! -
| -
|-
|}

===Options===
The options are the number of discrete and continuous variables and an optional time delay for per evaluation.

==Module Description==

=== Computation of the Objective ===

The single objective function ''f'' is a quadratic function. More precisely, ''f'' is computed as the square of all continuous and discrete variables.

''f''('''x''') = sumi(xi2)

This function is also referred to as the ''sphere function''.

==Usage==
-
==Source Code==

https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/modules/problems/ContinuousMOTestProblems.java

==References==

-

Documentation/Modules/ContinuousMOTestProblems

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Admin: /* Source Code */

Documentation/Modules/ContinuousMOTestProblems

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Documentation/Modules/ContinuousMOTestProblems

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Admin: Die Seite wurde neu angelegt: „==Summary== The module <code>ContinuousMOTestProblems</code> contains a set of multi-objective optimization problesm, which can be used to test optimization a…“

==Summary==

The module <code>ContinuousMOTestProblems</code> contains a set of multi-objective optimization problesm, which can be used to test optimization algorithms.

==Properties==

===General===

{| class="wikitable" style="text-align:left" border="2" cellpadding="5" cellspacing="5"
|-
! Algorithm
| deterministic test function
|-
! Design Variables
| Continuous design variables only
|-
! Objectives
| multi-objective for minimization
|-
! Constraints
| none
|-
! Boundaries
| problem dependent
|-
! Initial Search Region
| problem dependent
|-
! Typical X
| not set.
|-
|}

===Connections===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Starting at his module
| none
|-
! Ending at this module
| Connections of type <code>optimization</code>
|-
|}

===Actions===
{| class="wikitable" style="text-align:left" border="1" cellpadding="5" cellspacing="0"
|-
! Name !! Description
|-
! none
| -
|-
|}

===Options===
The options are the number of discrete and continuous variables and an optional time delay for per evaluation.

==Module Description==

=== Computation of the Objective ===

The single objective function ''f'' is a quadratic function. More precisely, ''f'' is computed as the square of all continuous and discrete variables.

''f''('''x''') = sumi(xi2)

This function is also referred to as the ''sphere function''.

==Usage==
-
==Source Code==

https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/modules/problems/ProblemSimple.java

==References==

-

Documentation/Notation

2019-04-07T12:31:38Z

Admin:

== Notation ==

As a maths parser is currently not used in this Wiki, we write math equations as formatted text:
* Scalars are written as small italic letters, e.g. ''f''
* Vectors are written as small bold letters, e.g. '''x'''.
* Matrices are written in capital bold letters, e.g. '''C'''.

== Symbols ==

:{| cellpadding="5" width="95%"
|-
| ''a'', '''a'''
| additionals, i.e. additional value(s)
|-
| ''f'', '''f'''
| objective function(s)
|-
| ''g'', '''g'''
| constraint(s)
|-
| ''x'', '''x'''
| design variable(s)
|-
|}

== Terminology ==

:{| cellpadding="5" cellspacing="0" width="95%" border="1"
|-
| ''additionals'' <div id="additionals"></div>
| Additionals '''a''' are additional values in a '''solution''' that are for information only and do not influence the optimization. For example, if an optimization problem returns an objective function that is a sum of several values, these values could be added to the solution as additional values '''a'''.
|-
| ''constraints'' <div id="constraints"></div>
| Constraints '''g''' are criteria that have to be fulfilled. OpenDino defines a constraint ''g'' as
* fulfilled, if ''g'' =< 0
* violated, if ''g'' > 0.

The most simple constraint handling in optimization is to add a penalty to the objective function if the constraint is vialoted, resulting in

''f'' + max(0, ''g'').
|-
| ''design variables'' <div id="design_variables"></div>
| The vector of design variables '''x''' may consist of real numbers (continuous variables), integers (discrete variables) or both (mixed variables).
|-
| ''objectives'' <div id="objectives"></div>
| An optimization problem must have at least one objective or one constraint. OpenDino requires that the objective(s) '''f''' is/are to be minimized. A maximization of a function ''k'' can be converted into minimization by using ''f'' = -''k''.
|-
| ''solution'' <div id="solution"></div>
| The term solution is used in optimization. One solution ('''x''','''f''','''g''','''a''') consists of a vector of design variables '''x''', the evaluated objective(s) '''f'''('''x'''), constraint(s) '''g'''('''x'''), and optionally of additional values '''a'''.
|-|}

Screenshots

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Admin:

== The Main Window ==

After starting software, the main '''GUI''' appears (Fig. 1). The figure contains an example for an optimization using 4 '''Module'''s. Each module has a name and an unique ID. The software is strictly modular. All functionality for automated optimization and machine learning is encoded in the modules. Here:

Module 1 (<code>OptAlgCMA</code>) is an optimization algorithm. It is connected via Module 3 (<code>Protocoller</code>) with Module 2 (<code>ProblemSimple</code>). Module 2 contains the optimization problem, i.e. the function to minimize. Here, it is a simple continuous, quadratic function.
Module 3 (<code>Protocoller</code>) is a protocoling module, i.e. it stores all evaluated solutions of an optimization run. These solutions can be visualized with Module 4 (<code>DataViewer</code>).
[[Image:MainGUI.png|frame|center|Figure 1: Main GUI of OpenDino]]

== Adding, Editing, and Connecting Modules ==

Right-clicking on the dotted area or on a module opens a dialogue as shown in Fig. 2. This dialogue supports:
* adding modules (if clicked on a free space)
* removing modules or changing the options of a module when clicking on a module
* adding/removing connections between modules
* checking and running modules
In Fig. 2, the right-click is made on Module 4, e.g. for setting the options of this module.

[[Image:ModuleRightclick.png|frame|center|Figure 2: Editing Modules]]

== Running an Optimization ==

Right-clicking Module 1 and selecting the option <code>Run</code> starts an optimization. The standard output of the optimization is given in the lower part of the main window in Fig. 3.

[[Image:Optimization.png|frame|center|Figure 3: Running an Optimization]]

== Postprocessing an Optimization ==

Right-clicking Module 4 and selecting the option <code>Run</code> starts an the data viewer. Figure 4 shows the evolution of the objective function ''f'' over the number of evaluations. As the objective function is a simple quadratic function (x^2), the minimal function value is zero, which is approximated by 1e-12.

[[Image:DataViewer.png|frame|center|Figure 4: Postprocessing an Optimization with the DataViewer module. ]]

Download

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Admin:

== Download Software ==

The executable jar file is given at

https://sourceforge.net/projects/opendino/files/

== Download Source Code ==

The software is version controlled using a Subversion (SVN) repository. Source code is located at

https://sourceforge.net/p/opendino/code

The URL above provides also the the download command (using subversion). In the subversion repository, theres is a "trunk" and "branches". The "trunk" contains the main development and should always at least compilable and executable.
"branches" are copies of the "trunk" for temporary modifications (see the [http://subversion.apache.org/ subversion homepage]) and should not be downloaded.

Downloading requires a [http://subversion.apache.org/ subversion client] or an appropriate plug-in for your IDE.

Documentation/Software/Requirements

2019-04-07T12:25:28Z

Admin:

OpenOpal is written in Java. The required java revisions are:

* Compiling: JDK 1.8 or higher
* Executing: JRE 1.8 or higher

Documentation/Software/Requirements

2019-04-07T12:24:34Z

Admin:

* Compiling: JDK 1.8 or higher
* Executing: JRE 1.8 or higher

Installation

2019-04-05T20:56:40Z

Admin: /* Writing a Shell Script */

== Download ==
The compiled software can be downloaded from [[Download]] page. As the software is written in Java, the compiled code runs on most platforms. The software consists of a single jar file <tt>opendino.jar</tt>.

== Running OpenDino ==

Start OpenDino by either:

* double clicking on the jar (this is simple, however, not activated on all platforms and console output may not be seen).
* open a Linux shell or Windows Command Line and enter one of the following two command in the same directory as file <tt>opendino.jar</tt> was downloaded (Windows often requires the latter command).
<pre>$ java -jar opendino.jar </pre>
<pre>$ java -classpath opendino.jar org.opendino.core.OpenDino</pre>
* to see additional command line options enter
<pre>$ java -jar opendino.jar -help </pre>

== Writing a Shell Script ==

The script assumes an OpenDino archive file <tt>opendino.jar</tt>.

For '''Windows''', create a file <tt>opendino.bat</tt> with the following content:

java -cp opendino.jar org/opendino/core/OpenDino %*

To use the batch file also from different directories, use the absolute path, e.g.:

java -cp C:\Programs\opendino.jar org/opendino/core/OpenDino %*

Now you can also double click project files (*.opl). When Windows ask for the program/app to open this file, choose the batch file written above.

For '''Linux''', create a file <tt>opendino.sh</tt> with the following content:

#!/bin/sh
java -classpath opendino.jar org.opendino.core.OpenDino $*

To make the file executable enter in the shell:

chmod u+x opendino.sh

Installation

2019-04-05T20:50:51Z

Admin: /* Running OpenDino */

== Download ==
The compiled software can be downloaded from [[Download]] page. As the software is written in Java, the compiled code runs on most platforms. The software consists of a single jar file <tt>opendino.jar</tt>.

== Running OpenDino ==

Start OpenDino by either:

* double clicking on the jar (this is simple, however, not activated on all platforms and console output may not be seen).
* open a Linux shell or Windows Command Line and enter one of the following two command in the same directory as file <tt>opendino.jar</tt> was downloaded (Windows often requires the latter command).
<pre>$ java -jar opendino.jar </pre>
<pre>$ java -classpath opendino.jar org.opendino.core.OpenDino</pre>
* to see additional command line options enter
<pre>$ java -jar opendino.jar -help </pre>

== Writing a Shell Script ==

The script assumes an OpenDino archive file <tt>opendino.jar</tt>. This filename can be replaced to match the actual name or by a filename with absolute path.

For '''Windows''', create a file <tt>opendino.bat</tt> with the following content:

java -cp opendino.jar org/opendino/core/OpenDino %*

For '''Linux''', create a file <tt>opendino.sh</tt> with the following content:

#!/bin/sh
java -classpath opendino.jar org.opendino.core.OpenDino $*

To make the file executable enter in the shell:

chmod u+x opendino.sh

Installation

2019-04-05T20:50:36Z

Admin:

== Download ==
The compiled software can be downloaded from [[Download]] page. As the software is written in Java, the compiled code runs on most platforms. The software consists of a single jar file <tt>opendino.jar</tt>.

== Running OpenDino ==

Start OpenDino with either:

* double clicking on the jar (this is simple, however, not activated on all platforms and console output may not be seen).
* open a Linux shell or Windows Command Line and enter one of the following two command in the same directory as file <tt>opendino.jar</tt> was downloaded (Windows often requires the latter command).
<pre>$ java -jar opendino.jar </pre>
<pre>$ java -classpath opendino.jar org.opendino.core.OpenDino</pre>
* to see additional command line options enter
<pre>$ java -jar opendino.jar -help </pre>

== Writing a Shell Script ==

The script assumes an OpenDino archive file <tt>opendino.jar</tt>. This filename can be replaced to match the actual name or by a filename with absolute path.

For '''Windows''', create a file <tt>opendino.bat</tt> with the following content:

java -cp opendino.jar org/opendino/core/OpenDino %*

For '''Linux''', create a file <tt>opendino.sh</tt> with the following content:

#!/bin/sh
java -classpath opendino.jar org.opendino.core.OpenDino $*

To make the file executable enter in the shell:

chmod u+x opendino.sh

Installation

2019-04-05T20:45:53Z

Admin:

== Download ==
The compiled software can be downloaded from [[Download]] page. As the software is written in Java, the compiled code runs on most platforms. The software consists of a single jar file <tt>opendino.jar</tt>.

== Start ==

Start OpenDino with either:

* double clicking on the jar (this is simple, however, not activated on all platforms and console output may not be seen).
* open a Linux shell or Windows Command Line and enter one of the following two command in the same directory as file <tt>opendino.jar</tt> was downloaded (Windows often requires the latter command).
<pre>$ java -jar opendino.jar </pre>
<pre>$ java -classpath opendino.jar org.opendino.core.OpenDino</pre>
* to see additional command line options enter
<pre>$ java -jar opendino.jar -help </pre>

== Writing a Shell Script ==

The script assumes an OpenDino archive file <tt>opendino.jar</tt>. This filename can be replaced to match the actual name or by a filename with absolute path.

For '''Windows''', create a file <tt>opendino.bat</tt> with the following content:

java -cp opendino.jar org/opendino/core/OpenDino %*

For '''Linux''', create a file <tt>opendino.sh</tt> with the following content:

#!/bin/sh
java -classpath opendino.jar org.opendino.core.OpenDino $*

To make the file executable enter in the shell:

chmod u+x opendino.sh

Installation

2019-04-05T20:35:46Z

Admin:

== Download ==
The compiled software can be downloaded from [[Download]] page. As the software is written in Java, the compiled code runs on most platforms. The software consists of a single jar file <tt>opendino.jar</tt>.

== Start ==

Start OpenDino with either:

* double clicking on the jar (this is simple, however, not activated on all plattforms and console output may not be seen).
* open a Linux shell or Windows Command Line and enter one of the following two command in the same directory as file <tt>opendino.jar</tt> was downloaded (Windows often requires the latter command).
<pre>$ java -jar opendino.jar </pre>
<pre>$ java -classpath opendino.jar org.opendino.core.OpenDino</pre>
* to see additional command line options enter
<pre>$ java -jar opendino.jar -help </pre>

== Writing a Shell Script ==

The script assumes an OpenDino archive file <tt>opendino.jar</tt>. This filename can be replaced to match the actual name or by a filename with absolute path.

For '''Windows''', create a file <tt>opendino.bat</tt> with the following content:

java -cp opendino.jar org/opendino/core/OpenDino %*

For '''Linux''', create a file <tt>opendino.sh</tt> with the following content:

#!/bin/sh
java -classpath opendino.jar org.opendino.core.OpenDino $*

To make the file executable enter in the shell:

chmod u+x opendino.sh

MediaWiki:Download

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Admin: Die Seite wurde neu angelegt: „Download“

Download

MediaWiki:Mainpage

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Admin: Die Seite wurde neu angelegt: „Main_Page“

Main_Page

Tutorials

2016-09-15T21:59:05Z

Admin: /* Java Code */

= Using OpenDino as a Library in Other Java Code =

OpenDino contains a graphical user interface for simple interaction (see e.g. the [Screenshots]).

Alternatively, OpenDino can be used as a library in other Java programs. All functionality of the software can be used such as the graphical plotting environment in the Module {{{DataViewer}}}.

=== Java Code ===

The tutorials can be directly executed as they are implemented in a Java main method.

{| class="wikitable" style="text-align:left" border="2" cellpadding="5" cellspacing="5"
|-
! Tutorial
! URL
|-
| single objective evolutionary optimization with output of the best solution
| https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/tutorial/TutorialSingleObjectiveOptimization.java
|-
| multi-objective evolutionary optimization with visualization and data storage
| https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/tutorial/TutorialMultiObjectiveOptimization.java
|}

Tutorials

2016-09-15T21:58:49Z

Admin: /* Using OpenDino as a Library in Other Java Code */

= Using OpenDino as a Library in Other Java Code =

OpenDino contains a graphical user interface for simple interaction (see e.g. the [Screenshots]).

Alternatively, OpenDino can be used as a library in other Java programs. All functionality of the software can be used such as the graphical plotting environment in the Module {{{DataViewer}}}.

== Java Code ==

The tutorials can be directly executed as they are implemented in a Java main method.

{| class="wikitable" style="text-align:left" border="2" cellpadding="5" cellspacing="5"
|-
! Tutorial
! URL
|-
| single objective evolutionary optimization with output of the best solution
| https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/tutorial/TutorialSingleObjectiveOptimization.java
|-
| multi-objective evolutionary optimization with visualization and data storage
| https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/tutorial/TutorialMultiObjectiveOptimization.java
|}

Documentation

2016-09-15T21:57:40Z

Admin: /* Developers' Guide */

== Requirements ==

* [[Documentation/Software/Requirements | Software Requirements]]

== Users' Guide ==

* [[Installation | Installation]]
* [[Screenshots | A First Introduction to OpenDino in the Screenshots]]
* [[Documentation/Notation | Symbols and Notation]]
* [[Documentation/Modules | Module Documentation]]

== Theory ==
* [[Documentation/DoE | Introduction to Design of Experiment]]
* [[Documentation/Optimization | Introduction to Automated Optimization]]
* [[Documentation/Machine_Learning | Introduction to Machine Learning]]

== Developers' Guide ==

* [[Download | Download]]
* [[Documentation/Compiling | Developing and Compiling]]
* [[Tutorials | Tutorials: Using OpenDino as a Library in other Java Code]]
* Concepts behind OpenDino
** [[Documentation/Modules/TheCode | The 3 Main Parts]]
** [[Documentation/Modules/IModule | Interface <code>IModule</code> - The Obligatory Interface for Modules]]
** [[Documentation/Modules/Interfaces | Interfaces of Modules]]
** [[Documentation/Modules/ClassesModules | Abstract Modules and Classes]]

== Various ==

* [[Documentation/Related | Links and Related Software]]

Documentation

2016-09-15T21:57:11Z

Admin: /* Developers' Guide */

== Requirements ==

* [[Documentation/Software/Requirements | Software Requirements]]

== Users' Guide ==

* [[Installation | Installation]]
* [[Screenshots | A First Introduction to OpenDino in the Screenshots]]
* [[Documentation/Notation | Symbols and Notation]]
* [[Documentation/Modules | Module Documentation]]

== Theory ==
* [[Documentation/DoE | Introduction to Design of Experiment]]
* [[Documentation/Optimization | Introduction to Automated Optimization]]
* [[Documentation/Machine_Learning | Introduction to Machine Learning]]

== Developers' Guide ==

* [[Download | Download]]
* [[Documentation/Compiling | Developing and Compiling]]
* [[Tutorials | Using OpenDino as a Library in other Java Code]]
* Concepts behind OpenDino
** [[Documentation/Modules/TheCode | The 3 Main Parts]]
** [[Documentation/Modules/IModule | Interface <code>IModule</code> - The Obligatory Interface for Modules]]
** [[Documentation/Modules/Interfaces | Interfaces of Modules]]
** [[Documentation/Modules/ClassesModules | Abstract Modules and Classes]]

== Various ==

* [[Documentation/Related | Links and Related Software]]

Tutorials

2016-09-15T21:56:44Z

Admin: moved Tutorials/UseAsLibrary to Tutorials

= Using OpenDino as a Library in Other Java Code =

OpenDino contains a graphical user interface for simple interaction (see e.g. the [Screenshots]).

Alternatively, OpenDino can be used as a library in other Java programs. All functionality of the software can be used such as the graphical plotting environment in the Module {{{DataViewer}}}.

== Tutorials ==

The tutorials can be directly executed as they are implemented in a Java main method.

{| class="wikitable" style="text-align:left" border="2" cellpadding="5" cellspacing="5"
|-
! Tutorial
! URL
|-
| single objective evolutionary optimization with output of the best solution
| https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/tutorial/TutorialSingleObjectiveOptimization.java
|-
| multi-objective evolutionary optimization with visualization and data storage
| https://sourceforge.net/p/opendino/code/HEAD/tree/trunk/src/org/opendino/tutorial/TutorialMultiObjectiveOptimization.java
|}

Tutorials/UseAsLibrary

2016-09-15T21:56:44Z

Admin: moved Tutorials/UseAsLibrary to Tutorials

#REDIRECT [[Tutorials]]

Documentation/DoE

2014-03-19T22:09:52Z

Admin:

=Design of Experiments=

== Introduction ==
Design of Experiments ('''DoE''') defines methods of how to plan and arrange experiments.
We explain the method using the example of a baker in a bakery.
The baker wants to analyze the effect of different settings of his oven to the quality of the resulting bread by conducting several experiments.

=== Target Values ===

We define the targets of experiments (here to improve the quality of the bread) as '''target values'''.

=== Input Parameters ===

All sources of influence on the target values are referred to as '''factors'''.
Three different groups of factors are distinguished:

# ''noise factors'': These factors cannot be controlled or controlling these factors is too expensive. As an example, the temperature and humidity in the bakery depends on the season and weather and cannot be controlled.
# ''control factors'': These factors can be controlled. However, their influence on the target value is not of interest and thus, these factors are set constant. For example, the baker wants to leave his recipe for the bread unchanged.
# ''signal factors'': These factors can be controlled. Their influence is of interest and their values are modified in the experiments.

DoE is important when we have to analyze a process with multiple signal factors, in order to determine their effect on the target value(s).

'''''Note:''' In the following, we will only consider signal factors. For simplicity, they are referred to as factors.''

=== Factor Levels ===

For each factor, we may want to analyze different values. For example, we want to analyze an oven temperature of 185, 190, 195, and 200oC and a baking time of 45, 50, 55 and 60 minutes. These different values are referred to as ''factor levels''.

=== Experiment ===

One experiment is one combination of different factor levels and the corresponding target values for the experiment. For example, one experiment may be a temperature of 190oC and a baking time of 60 min. One experiment is also referred to as one '''factor level combination'''.

=== DoE Plan ===

The DoE plan defines which experiments are to be conducted and in which sequence they are conducted. The sequence may be important as environmental conditions like temperature may effect the target values as well as hysteresis effects.

DoE plans differ in number of total experiments and the order of the experiments. The number of experiments strongly influences the information gain and the trust in the experiments.

As references for DoE plans we recommend [ [[#klepp | 1]], [[#wikidoe | 2]] ].

== DoE Plans ==
A DoE plan can be considered as a set of rules describing how to choose factors, the number of factor levels, factor level values or value distributions, etc.. In the following, two different DoE plans are outlined.

=== Full Factorial ===

[[Image:DoEFullFactorial.png|frame|right|A two factor Full Factorial plan. Each square represents one possible experiment. The "X" denote the actual experiments that will be scheduled.]]
For given numbers of factor levels for each variable, a limited number of different experiments can be conducted. A DoE plan with all possible experiments is referred to as '''Full Factorial''' plan: The number of factor levels may vary for the factors. All other plans use only a subset of all possible experiments.

=== Latin Hypercube ===

[[Image:DoELatinHypercube.png|frame|right|A two factor Latin Hypercube plan. Each square represents one possible experiment. The "X" denote the actual experiments that will be scheduled.]]

Compared to the Full Factorial plan, the Latin Hypercube (LHC) sampling dramatically reduces the number of experiments (see e.g. [ [[#wikilhc | 3]] ]).
So it is most useful for expensive experiments, whether they be money or time intensive.
Latin Hypercube requires the same number of factor levels for all factors.
The number of experiments of a LHC plan is equal to the number of factor levels and is independent on the number of factors.
This makes it very scalable (O(1)) in respect of number of factors.

The philosophy of LHC is to use every factor level exactly once.
The figure on the right demonstrates a possible plan for 4 factor levels. As each factor level is only used once, each row and column in the figure must contain exactly one cross.
Thus, LHC guaranties that for each factor, the entire range of factor levels is used.

== Common Settings for all DoE Plans ==

The following list explains common settings for all DoE plans.

=== Number of Iterations ===

The testing of a set of experiments that a DoE plan proposes is referred to as one ''iteration''.
If we would schedule three ''iterations'', each experiment (i.e. a certain factor level combination) in the set is tested exactly three times.

Using more than one iteration is only necessary if the system to analyze is noisy (i.e. the target values are noisy), may contain a drift while testing, or produces hysteresis.

=== Random Scheduling ===

[[Image:DoERandomBlock.png|thumb|250px|right|Four different scheduling plans of six experiments resulting from DoE plan with three experiments and two iterations. The small double-bordered boxes outline the blocks.]]

Without ''random scheduling'', the experiments are tested in a fixed order.
With ''randomization'' activated, a random sequence for the experiment is set up.
To what extend the randomization occurs depends on whether ''block building'' is active or not.

''Random scheduling'' may help to analyze effects like hysteresis, i.e. the previous experiment effects the outcome of the current experiment.
If we run multiple iterations, the effect of hysteresis decreases because of statistical reasons.

=== Block Building ===

If ''block building'' is activated, all experiments of one iteration are tested. Then, all experiments of the next iteration are tested.
If ''block building'' is '''de'''activated, experiments of different iterations may follow each other.
''Block building'', like ''random scheduling'', may reduce the effect that may occur because of the order in which the experiments get scheduled.
While ''random scheduling'' may reduce the effect of subsequent experiments, activating ''block building'' may reduce the effect caused by external (long term) influences.
These influences can be quality of the used materials, which may vary over time or depend on the supplier, human interaction with the machines or the accuracy of machines of the same type used for the experiments.

== DoE in OpenDino ==

A description for DoE in OpenDino can be found in the module documentation (see [[Documentation/Modules/DoePlanner]]).

== References ==

1 Wilhelm Kleppmann, Taschenbuch Versuchsplanung (4. Auflage) - Produkte und Prozesse optimieren, Carl Hanser Verlag Muenchen Wien, 2006

2 http://en.wikipedia.org/wiki/Design_of_experiments

3 http://en.wikipedia.org/wiki/Latin_hypercube

Documentation/Optimization

2014-03-19T22:09:15Z

Admin:

=Optimization=

== Introduction ==

Optimization searches for the best solution '''x'''* to a problem and can be defined as

'''x'''* = argmin (''f''('''x'''))

where ''f'' is the function to be optimized (the objective function) and '''x''' are the design variables for which optimal settings have to be found.

This definition can be extended to multiple objectives '''f''' and by constraints.

== Some Important Expressions ==
=== Manual vs. Automated Optimization ===

Optimization is an everyday task. For example, searching the fastest way to work or home is an optimization problem. We speak about ''automated'' optimization, if a computer algorithm solves the problem in an automated fashion, i.e. without user interaction.

=== Objective Function ===
Optimization typically searches for the optimal solution to a problem. For automated optimization, the problem must be encoded in a mathematical function ''f'', which has to be either minimized or maximized. In OpenDino, only minimization is considered as maximization can be expressed as:

max(f) = - min(-f)

=== Design Variables ===
The optimal solution is searched by modifying the design variables '''x'''. The objective function depends on the design variables: f = f('''x''').

=== Multiple Objectives ===

If multiple objectives '''f''' should be optimized, then a multi-objective optimization problem has to be solved. Some optimization algorithms search concurrently for multiple compromise solutions for the objectives (Pareto optimization) or a single compromise solution, defined as a weighed sum of all objectives.

=== Constraints ===

While the optimization algorithm tries to minimize all objectives, constraints simply have to be fulfilled.

For example, the maximal stress in a truss should not exceed a certain limit. If the stress is below the limit, no advantage is gained. If the stress is above the limit, the solution is typically constrained by a penalty value, which increases with increasing constraint violation.

=== Direct vs. Indirect Search ===

In OpenDino, we implement optimization algorithms that search a problem in an iterative fashion, i.e. by computing several different solutions to the problem. The best solution is returned. This iterative search can be either direct or indirect:

'''Direct''' search uses only direct information (i.e. the objective and constraint value(s).
'''Indirect''' algorithms use indirect information (i.e. gradient and/or higher order derivative information of the objective(s) and constraint(s)).

=== Stochastic vs. Deterministic Algorithms ===

While '''stochastic''' algorithms such as Evolutionary Algorithms and Particle Swarm use random values in their search method, '''deterministic''' algorithms like the Simplex Method or gradient based search do not.

Documentation/Compiling

2014-03-19T22:08:10Z

Admin:

Check the [[Documentation/Software/Requirements | software requirements]] first! Then [[Documentation/Download/SVN | download]] the source code.

== Using an IDE ==

OpenDino is currently developed with [http://www.netbeans.org/ NetBeans], as many windows are generated with the Swing framework.

However, other platforms such as Eclipse [http://www.eclipse.org/ Eclipse] can also be used.

Compiling and executing the software in a shell is also supported using <code>Makefile</code>s as build system.

== Compiling in a Shell ==

Two platforms are currently tested:

<ul>
<li>Linux: 
A typical standard implementation of Linux with a Java JDK should contain all required programs for compiling and running OpenDino.
</li>
<li>Windows: 
[http://www.cygwin.com/ Cygwin (R)] is used to compile OpenDino on Windows(R) platforms.
</li>
</ul>
Compiling on other platforms has not been tested.

=== Creating the Makefile ===

The Makefile is automatically generated by the script <tt>createMakefile</tt>.
In a shell, go to the subfolder <tt>opendino/trunk</tt> and execute:
<pre>$ ./createMakefile</pre>

Enter
<pre>$ make</pre>
to list the possible actions.

=== Compiling the Source Code & Executing OpenDino ===

For Linux systems enter
<pre>$ make javac
$ make gui</pre>
to compile and start OpenDino in GUI mode.

For Windows systems enter
<pre>$ make javac_cygwin
$ make gui</pre>

Documentation/Machine Learning

2014-03-19T22:07:16Z

Admin:

== Introduction ==

Given some data, machine learning infers relations among them. Well known representatives of machine learning are Artificial Neural Networks and Support Vector Machines.

The goal in OpenDino is to use these techniques to build models either to speed up optimization runs or to post-process the result both [[Documentation/DoE | DoE]] and [[Documentation/Optimization | optimization]].

Documentation/Modules/TheCode

2014-03-19T22:06:50Z

Admin:

OpenDino consists of 3 parts

# the GUI
# the Core
# many Modules

== The GUI ==

The graphical user interface (GUI) allows setting complex systems by clicking and dragging. It allows to add or remove <code>Modules</code> and to define their interactions by so called <code>Connections</code>.

== The Core ==

The <code>Core</code> handles all file operation as well as provides the basic functionality such that <code>Modules</code> can be implemented.

== The Modules ==

The <code>Modules</code> contain all functionality such as optimization algorithms, protocol functions, visualization of results, and optimization problems.

All <code>Modules</code> must implement the interface [ <code>IModule</code>]

Documentation/Modules/IModule

2014-03-19T22:06:25Z

Admin:

=Interface <tt>IModule</tt>=

All <tt>Modules</tt> in OpenDino must implement the Interface <tt> IModule</tt>.
The interface contains a set of methods. The purpose and the functionality of these methods is described in the following sections.

Furthermore, the <tt>Modules</tt> must have a class <tt>Options</tt> that comprises all user settings as simple types (<tt>boolean</tt>, <tt>long</tt>, <tt>double</tt>, <tt>String</tt>) or vectors/arrays of those types. The options are automatically read using Java Reflection API.

It is essential for a correct program execution to ensure that the guidelines are exactly followed by the <tt>Modules</tt> programmers.

== Method <tt>Options getOptions()</tt> ==

# Functionality
#* returns the current <tt>Options</tt> class.
#* ensure that cloned <tt>Options</tt> are returned!
# Objectives
#* returning a clone of the <tt>Options</tt> ensures that changing <tt>Options</tt> outside the <tt>Module</tt> has no impact.
# Sample Code
<pre>
public Options getOptions() {
return (Options) options.clone();
}
</pre>

== Method <tt>void setOptions(Options o)</tt> ==
# Functionality
#* sets the <tt>Options</tt> for a <tt>Module</tt>
#* ensure that <tt>Options</tt> are cloned before they are set!
# Objectives
#* setting a clone of the <tt>Options</tt> ensures that changing <tt>Options</tt> outside the <tt>Modules</tt> has no impact.
# Sample Code
<pre>
public void setOptions(Options o) {
options = (Options) o.clone();
}
</pre>

== Method <tt>String check() </tt> ==
# Functionality
#* check, if this <tt>Modules</tt> is set up correctly, i.e. checks if
#** the <tt>Options</tt> are correct?
#** all required <tt>Connections</tt> of the <tt>Modules</tt> are available (e.g. an optimization algorithm may require one connection to a problem)
#** the settings (e.g. Options) agree with the properties of connected <tt>Modules</tt>?
#* checking requires several steps:
#*# if the <tt>Modules</tt> is connected to other <tt>Modules</tt>, it should first
#*#* call <tt>check()</tt> of the connected <tt>Modules</tt> and
#*#* return a message, if any connected <tt>Modules</tt>’s check is unsuccessful (i.e. a non-empty string is returned).
#*# if checking the connected <tt>Modules</tt> was successful, the <tt>Modules</tt> should check itself and return a non-empty string if checking fails.
# Properties
#* checks are usually fast
# Return Value
#* empty string “” if successful, else a message
#Sample Code for Optimization:
<pre>
public String check(){

s = "";

// 1. check connected modules
for (Object o: evaluables) {
String so=((IModule)o).check();
if (so.length > 0){
s += “checking connected module with ID “ +
(IModule o).getID() + “ failed with message: \\n” + so;
}
}
if (s.length > 0){
return “checking connected modules failed: \\n” + s;

// 2. check this module, return String if not successful
if (opt.verbose > 3)
s+= "Option verbose must be between 0 and 3, but is set to " + opt.verbose + "\\n";
// …

return s;
}
</pre>

== Method <tt>String init(long index)</tt> ==

Currently the <tt>index</tt> is not used!

# Functionality
#* initialize <tt>Modules</tt> such that executing optimizations or learning processes is possible.
#* Initialization requires several steps:
#*# check if the argument (index) has changed compared to the time of the last check (see also example below). If the argument has not changed, the system has not changed and a new initialization is not necessary. Thus, return the message of the previous check and do no further checking.
#*# If the <tt>Modules</tt> is connected to other Modules, it should first
#*#* call the <tt>init()</tt> of the connected <tt>Modules</tt> and
#*#* return a message, if any connected <tt>Modules</tt> fails to initialize
#*# If the initialization of the connected <tt>Modules</tt> is successful, it should then get the properties of the connected <tt>Modules</tt>
#*#* return a message, if any connected <tt>Modules</tt> fails to return properties.
#*# Initialize itself and return a string if initialization fails.
# Objectives
#* initialization should never lead to exceptions.
#* It must not call <tt>check()</tt>!
# Properties
#* the <tt>init()</tt> method is the only method of <tt>IModule</tt> that may require CPU time (e.g. for loading a large data source). Thus this method should not be called too often.
# Advanced Features
#* the<tt>Modules</tt> should check if initialization is necessary. Typically initialization is necessary when
#** the <tt>Options</tt> of the<tt>Modules</tt> are changed
#** the connections of the<tt>Modules</tt> changed
#** the properties of the connected Modules changed
#* if nothing changed, it should not initialize in order to save computer resources
# Return Value
#* empty string “” if successful, else a message
# Sample Code for Optimization:
<pre>
public string init(long init_id){

s = "";
if (this.init_id == init_id){
return [the message of the last initialization];

this.init_id = init_id;

for (Object o: evaluables) {
String so=((IModule)o).init(init_id);
if (so.length > 0){
s += “initializing connected module with ID “ +
(IModule o).getID() + “ failed with message: \\n” + so;
}
}
if (s.length > 0){
return “initializing connected modules failed: \\n” + s;

// initialize this module, return String if not successful
// …

return s;
}
</pre>

'''Summary'''

Properties of a <tt>Module</tt> should only change when the Method <tt>init()</tt> is called.
This guarantees that <tt>Modules</tt> do not change while they are checked or other <tt>Modules</tt> are running!

== Method <tt>int getID()</tt> ==
# Functionality
#* gets the ID for this <tt>Module</tt>.
# Objectives
#* the ID is important for debugging, e.g. <tt>Modules</tt> can identify to which other <tt>Modules</tt> they are connected.

== Method <tt>void setID(int id)</tt> ==
# Functionality
#* sets the ID for this <tt>Module</tt>.
# Objectives
#* IDs should be set at the time of instantiation and should not be modified.
#* the ID is important for debugging, e.g. <tt>Modules</tt> can identify to which other <tt>Modules</tt> they are connected.

== Method <tt>String getInfo()</tt> ==
# Functionality
#* Returns a String in HTML or plain text, describing the module
#* A<tt>Modules</tt> such as an optimization algorithm should return the problem properties when it this<tt>Modules</tt> is called.
# Objectives
#* get information about<tt>Modules</tt> properties for checking or debugging user settings

Documentation/Modules/Interfaces

2014-03-19T22:06:03Z

Admin:

==Basics==

The basic element of each OpenDino optimization or learning environment is still the <code>Module</code>. Each optimization problem, each optimization algorithm and each learning algorithm is a module. A class that is used as a module must implement the interface <code>org.opendino.core.modules.IModule</code> (see [[Documentation/IModule | IModule]]).

The whole structure of modules in OpenDino is based on interfaces. The reason for this is that classes that are used as <code>Modules</code> may be part of a class hierarchy and thus they cannot inherit from a basic module class.

There are <code>Modules</code> that can be run, e.g. optimization algorithms. All these modules implement the interface <code>org.opendino.core.modules.IRunnable</code>.

Each optimization setup consists of at least two linked <code>Modules</code>: a problem and an optimizer. The interfaces for these modules are <code>org.opendino.core.modules.optimization.IEvaluable</code> and <code>IEvaluator</code>, respecively. The corresponding pair for a learning task is <code>org.opendino.core.modules.learning.IDataSource</code> and <code>IDataSink</code>.

Modules can be linked between other modules. E.g. the protocoller is a <code>Module</code> that you can link between an optimizer and a problem. In such a case, all <code>Modules</code> except the optimizer must implement the interface <code>Evaluable</code> and all modules except the problem must implement the interface <code>IEvaluator</code>. This means that the modules form a chain that looks like this (here, two intermediate modules are shown):
<pre>
[IEvaluator]
|
[IEvaluable and IEvaluator]
|
[IEvaluable and IEvaluator]
|
[IEvaluable]
</pre>
where the topmost evaluator is the optimizer and the bottommost evaluable is the optimization problem.
The meaning of this picture is that the optimizer produces points and sends them through the chain to the problem. The problem evaluates the points, assigning each point its objective value(s). These are read by the optimizer which then tries to create better points, etc. The same kind of interaction chain is possible for learners and data sources. But there, the information flow is mainly in unidirectional: from the data source to the data sink.

==The Interfaces==

===IModule===

This is the basic interface implemented by all modules. It contains methods for the initialisation and checking of the module and for the management of the options of the module.

===IRunnable===

This interfaces is implemented by all modules that can be run. When a module is run, it executes its optimization algorithm. In the future this could be any kind of algorithm. The interface contains methods to start and stop the execution of the module.

===IEvaluable===

Every module that represents an optimization problem implements the interface IEvaluable. This means that the module can evaluate a given solution. To that end, it looks at the design variables of a solution object it gets from an optimizer and sets the objective value(s) of that object accordingly.

If a module is linked before a problem module, it must implement IEvaluable and delegate the evaluation of the solutions to the problem module. Thus, the optmimizer doesn't know if the module right after it is the problem itself or an intermediate module, it just calls the evaluate method of its successor module.

IEvaluable contains methods to evaluate one solution or an array of solutions and a method to get the properties of the problem.

===IEvaluator===

A module that can call an evaluable to evaluate solutions implements the interface IEvaluator. Normally, an evaluator is an optimizer module, but any intermediate module between the optimizer and the problem must implement this interface as well. Otherwise, it wouldn't be able to call the problem to evaluate the solutions.

IEvaluator contains methods to get and set the references to the evaluables and to check if these links are OK.

===IDataSource===

A module that provides data. This can be a file reader, the protocoller, or other modules that can produce or store data. The getData() method is the most important one.

===IDataSink===

A module implementing this interface represents a learner. It is linked with a data source and processes the given data. Very different kinds of processing are possible. A data viewer can just display the data, a neural network can learn a function from the data or a statistics module can compute some statistics on the data etc.

Documentation/Modules

2014-03-19T22:05:23Z

Admin:

'''Modules''' contain all the functionality for optimizing and learning. One '''Modules''' may contain an optimization algorithm, an artificial neural network, or a problem to optimize.

Here is a list of documented modules in OpenDino. Further modules may exist, but may not yet be documented.

== Single Objective Optimization Algorithms ==

=== Indirect, Deterministic Algorithms ===
Not implemented, yet.

=== Direct, Deterministic Algorithms ===
* [[Documentation/Modules/OptAlgSIMPLEX | <code>OptAlgSIMPLEX</code>: Nelder-Mead Simplex Algorithm]]
=== Indirect, Stochastic Algorithms ===
* Evolutionary Algorithms
** [[Documentation/Modules/OptAlgOpO|<code>OptAlgOpO</code>: 1+1 Evolution Strategy with 1/5 Success Rule]]
** [[Documentation/Modules/OptAlgCMA|<code>OptAlgCMA</code>: A Multi-member Evolution Strategy with Covariance Matrix Adaptation]]

== Single and Multi-Objective Optimization Algorithms ==

=== Indirect, Stochastic Algorithms ===
* Evolutionary Algorithms
** [[Documentation/Modules/OptAlgMoCMA | <code>OptAlgMoCMA</code>: Elitist Evolution Strategy with Covariance Matrix Adaptation]]
* Particle Methods
** [[Documentation/Modules/OptAlgMOPSO | <code>OptAlgMOPSO</code>: Particle Swarm Optimization Algorithm]]

== Design of Experiments ==
* [[Documentation/Modules/DoePlanner | <code>DoePlanner</code>: A Module Containing Different DoE plans]]
* [[Documentation/Modules/RandomSampling | <code>RandomSampling</code>: Uniform Random Sampling]]

== Optimization in General ==
* [[Documentation/Modules/BoundHandler | <code>BoundHandler</code> For Optimization Algorithms Without Bound Handling]]

== Optimization Problems ==
* [[Documentation/Modules/ProblemSimple | <code>ProblemSimple</code> A Simple Test Problem]]
* [[Documentation/Modules/ContinuousTestProblems | <code>ContinuousTestProblems </code>: A Set of Single Objective Test Problem]]
* [[Documentation/Modules/ContinuousMOTestProblems | <code>ContinuousMOTestProblems </code>: A Set of Multi-Objective Test Problem]]

== Machine Learning ==
* [[Documentation/Modules/NeuralNetwork | <code>NeuralNetwork</code>: Artificial Neural Network]]

== Miscellaneous Modules ==
* [[Documentation/Modules/SurrogateManager | <code>SurrogateManager</code>: A Framework for Surrogate Managing]]

Documentation

2014-03-19T22:04:54Z

Admin:

== Requirements ==

* [[Documentation/Software/Requirements | Software Requirements]]

== Users' Guide ==

* [[Installation | Installation]]
* [[Screenshots | A First Introduction to OpenDino in the Screenshots]]
* [[Documentation/Modules | Module Documentation]]

== Theory ==
* [[Documentation/DoE | Introduction to Design of Experiment]]
* [[Documentation/Optimization | Introduction to Automated Optimization]]
* [[Documentation/Machine_Learning | Introduction to Machine Learning]]
* [[Documentation/Notation | Symbols and Notation]]

== Developers' Guide ==

* [[Download | Download]]
* [[Documentation/Compiling | Developing and Compiling]]

* Concepts behind OpenDino
** [[Documentation/Modules/TheCode | The 3 Main Parts]]
** [[Documentation/Modules/Execution | Module Execution]]
** [[Documentation/Modules/IModule | Interface <code>IModule</code> - The Obligatory Interface for Modules]]
** [[Documentation/Modules/Interfaces | Interfaces of Modules]]
** [[Documentation/Modules/ClassesModules | Abstract Modules and Classes]]

== Various ==

* [[Documentation/Related | Links and Related Software]]

Screenshots

2014-03-19T22:04:15Z

Admin:

== The Main Window ==

After starting software, the main '''GUI''' appears (Fig. 1). The figure contains an example for an optimization using 4 '''Module'''s. Each module has a name and an unique ID. The software is a strictly modular. All functionality for automated optimization and machine learning is encoded in the modules. Here:

Module 1 (<code>OptAlgCMA</code>) is an optimization algorithm. It is connected via Module 3 (<code>Protocoller</code>) with Module 2 (<code>ProblemSimple</code>). Module 2 contains the optimization problem, i.e. the function to minimize. Here, it is a simple continuous, quadratic function.
Module 3 (<code>Protocoller</code>) is a protocoling module, i.e. it stores all evaluated solutions of an optimization run. These solutions can be visualized with Module 4 (<code>DataViewer</code>).
[[Image:MainGUI.png|frame|center|Figure 1: Main GUI of OpenDino]]

== Adding, Editing, and Connecting Modules ==

Right-clicking on the dotted area or on a module opens a dialogue as shown in Fig. 2. This dialogue supports:
* adding modules (if clicked on a free space)
* removing modules or changing the options of a module when clicking on a module
* adding/removing connections between modules
* checking and running modules
In Fig. 2, the right-click is made on Module 4, e.g. for setting the options of this module.

[[Image:ModuleRightclick.png|frame|center|Figure 2: Editing Modules]]

== Running an Optimization ==

Right-clicking Module 1 and selecting the option <code>Run</code> starts an optimization. The standard output of the optimization is given in the lower part of the main window in Fig. 3.

[[Image:Optimization.png|frame|center|Figure 3: Running an Optimization]]

== Postprocessing an Optimization ==

Right-clicking Module 4 and selecting the option <code>Run</code> starts an the data viewer. Figure 4 shows the evolution of the objective function ''f'' over the number of evaluations. As the objective function is a simple quadratic function (x^2), the minimal function value is zero, which is approximated by 1e-12.

[[Image:DataViewer.png|frame|center|Figure 4: Postprocessing an Optimization with the DataViewer module. ]]

== References ==

Input file behind screen shots is stored in the SVN repository ToDo: Link.

Installation

2014-03-19T22:03:30Z

Admin: /* Start */

== Download ==
The compiled software can be downloaded from [[Download]] page. As the software is written in Java, the compiled code runs on most platforms. The software consists of a single jar file <tt>opendino.jar</tt>.

== Start ==

Start OpenDino with either:

* double clicking on the jar (this is simple, however, not activated on all plattforms and console output may not be seen).
* open a Linux shell or Windows Command Line and enter one of the following two command in the same directory as file <tt>opendino.jar</tt> was downloaded (Windows often requires the latter command).
<pre>$ java -jar opendino.jar </pre>
<pre>$ java -classpath opendino.jar org.opendino.core.OpenDino</pre>
* to see additional command line options enter
<pre>$ java -jar opendino.jar -help </pre>

== Writing a Shell Script ==

The script assumes an OpenDINO archive file <tt>opendino.jar</tt>. This filename can be replaced to match the actual name or by a filename with absolute path.

For '''Windows''', create a file <tt>opendino.bat</tt> with the following content:

java -cp opendino.jar org/opendino/core/OpenDINO %*

For '''Linux''', create a file <tt>opendino.sh</tt> with the following content:

#!/bin/sh
java -classpath opendino.jar org.opendino.core.OpenDINO $*

To make the file executable enter in the shell:

chmod u+x opendino.sh

Installation

2014-03-19T22:02:57Z

Admin: /* Start */

== Download ==
The compiled software can be downloaded from [[Download]] page. As the software is written in Java, the compiled code runs on most platforms. The software consists of a single jar file <tt>opendino.jar</tt>.

== Start ==

Start OpenDino with either:

* double clicking on the jar (this is simple, however, not activated on all plattforms and console output may not be seen).
* open a Linux shell or Windows Command Line and enter one of the following two command in the same directory as file <tt>opendino.jar</tt> was downloaded (Windows often requires the latter command).
<pre>$ java -jar opendino.jar </pre>
<pre>$ java -classpath opendino.jar org.opendino.core.OpenDINO</pre>
* to see additional command line options enter
<pre>$ java -jar opendino.jar -help </pre>

== Writing a Shell Script ==

The script assumes an OpenDINO archive file <tt>opendino.jar</tt>. This filename can be replaced to match the actual name or by a filename with absolute path.

For '''Windows''', create a file <tt>opendino.bat</tt> with the following content:

java -cp opendino.jar org/opendino/core/OpenDINO %*

For '''Linux''', create a file <tt>opendino.sh</tt> with the following content:

#!/bin/sh
java -classpath opendino.jar org.opendino.core.OpenDINO $*

To make the file executable enter in the shell:

chmod u+x opendino.sh

Datei:Opendino 0.1.jar

2014-03-19T22:01:36Z

Admin: