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Difference between revisions of "VIATRA/DSE"

(User's guide)
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* [[VIATRA/DSE/UserGuide/API|Usage of the API]]
 
* [[VIATRA/DSE/UserGuide/API|Usage of the API]]
 
* [[VIATRA/DSE/UserGuide/Statecoders|State coders]]
 
* [[VIATRA/DSE/UserGuide/Statecoders|State coders]]
* [[VIATRA/DSE/UserGuide/Strategy|Strategies]]
 
** [[VIATRA/DSE/UserGuide/NewAlgorithms|Developing new algorithms]]
 
** [[VIATRA/DSE/UserGuide/BasicAlgorithms|Basic algorithms]]
 
** [[VIATRA/DSE/UserGuide/Genetic|Genetic algorithm (NSGA-II)]]
 
* [[VIATRA/DSE/UserGuide/DesignSpace|Design space]]
 
 
* Examples
 
* Examples
 
** [[VIATRA/DSE/UserGuide/BPMNExample|BPMN example]]
 
** [[VIATRA/DSE/UserGuide/BPMNExample|BPMN example]]
 
* [[VIATRA/DSE/Publications|Publications]]
 
* [[VIATRA/DSE/Publications|Publications]]
 
  
 
== Important links ==
 
== Important links ==

Revision as of 09:29, 25 July 2016

VIATRA Design Space Exploration Framework (VIATRA-DSE)

Design space exploration (DSE) is a multi-criteria, search-based design process, which searches for good enough solutions within the possible design alternatives.

VIATRA-DSE is Java based framework for model-driven, rule-based design space exploration with the following main concepts:

  • Model-driven: the problem representation is a typed attributed graph, which well fits in the processes of model-driven approaches, where models are typically graph-like structures and constraints are graph patterns. VIATRA-DSE stores the models (design candidates) as EMF models and captures constraints as VIATRA Query graph patterns.
  • Rule-based: in certain scenarios we not only interested in a solution (e.g. sudoku), but we also need to know how to reach that solution (e.g. Rubik's cube). In VIATRA-DSE rules are defined as graph-transformation rules, while solutions are defined as a sequence of rules applications (trajectory) which transforms the initial model into a desired one.
  • Multi-Objective Optimization: VIATRA-DSE is able to handle multiple objective functions, both hard objectives (shall be satisfied by a goal state) and soft objectives (should be optimized), which can be derived from the model (graph pattern, simulation, etc.) or from the trajectory.
  • Meta-heuristic strategies: meta-heuristic search techniques are widely used in optimization problems. VIATRA-DSE comes with a set of built-in exploration strategies such as depth and breadth first search, fixed-priority strategy, hill climbing strategy, evolutionary algorithms (e.g. NSGA-II and PESA) and more will come. Developers can easily integrate their own strategies.


User's guide

Important links

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