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= Welcome to '''OpenDino''' = | = Welcome to '''OpenDino''' = | ||
| − | '''OpenDino ''' is an open source platform for optimization, design of experiment and learning. | + | '''OpenDino ''' is an open source platform for automated optimization, design of experiment and learning, written in Java. |
| − | The | + | The source code as well as as an executable can be found on [http://sourceforge.net/projects/opendino/ sourceforge]. The source code is structured in 3 layers: A core providing the basic backbone, Modules containing all algorithms, and a graphical user interface (GUI). This simplifies the integration of new algorithms as they are represented by additional "Modules". |
| − | + | Implemented Modules | |
| − | + | * Evolutionary optimization algorithms (CMA-ES, (1+1)-ES, Swarm Algorithms) | |
| − | + | * Deterministic optimization algorithm (SIMPLEX) | |
| − | + | * Learning (feed-forward Artificial Neural Net) | |
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| − | + | * Design of Experiments (full factorial design, Latin Hypercube, I-Optimal Design) | |
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| − | + | * Problems (test functions, interface to external programs (solvers), distributed and parallel execution) | |
| − | * | ||
| − | + | * Other modules (data storage and visualization) | |
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| − | + | Source Code | |
| − | * | + | * http://sourceforge.net/projects/opendino/ |
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Contact: | Contact: | ||
* [mailto:help@opendino.org help@opendino.org] | * [mailto:help@opendino.org help@opendino.org] | ||
Aktuelle Version vom 18. Februar 2016, 21:28 Uhr
Welcome to OpenDino
OpenDino is an open source platform for automated optimization, design of experiment and learning, written in Java.
The source code as well as as an executable can be found on sourceforge. The source code is structured in 3 layers: A core providing the basic backbone, Modules containing all algorithms, and a graphical user interface (GUI). This simplifies the integration of new algorithms as they are represented by additional "Modules".
Implemented Modules
- Evolutionary optimization algorithms (CMA-ES, (1+1)-ES, Swarm Algorithms)
- Deterministic optimization algorithm (SIMPLEX)
- Learning (feed-forward Artificial Neural Net)
- Design of Experiments (full factorial design, Latin Hypercube, I-Optimal Design)
- Problems (test functions, interface to external programs (solvers), distributed and parallel execution)
- Other modules (data storage and visualization)
Source Code
Contact:
