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![ Marrying Optimization and Model Transformations
Based on Eclipse platform
Eclipse EMF as modeling environment
Henshin as graph transformation engine
MOEA Framework for SBO algorithms
Search-Based Model Transformations
6
Marrying Optimization and Model Transformations (MOMoT)
MOMoT
Problem Instance Model
Objectives
Constraints
Transformation Rules
Exploration Configuration
Result Models
Objective Values
Generic
Encoding
Search-based
Exploration
Result
Generation
Constraint Values
Rule Orchestrations
Exploration Statistics
Objective and
Constraint Evaluation
Transformation
Orchestration
[1] MOMoT Project: http://martin-fleck.github.io/momot/
[2] Eclipse Modeling Framework: https://eclipse.org/modeling/emf/
[3] Henshin Project: https://www.eclipse.org/henshin/
[4] MOEA Framework: http://moeaframework.org/
MOEA](https://image.slidesharecdn.com/2016-07-04icmtslideshare-160714204226/85/ICMT-2016-Search-Based-Model-Transformations-with-MOMoT-17-320.jpg)
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ICMT 2016: Search-Based Model Transformations with MOMoT
- 1. Search-Based Model Transformationswith MOMoT http://martin-fleck.github.io/momot/ ICMT, Vienna 4th July 2016 A Tool Demonstration Martin Fleck, Javier Troya, Manuel Wimmer
- 2. Problem Statement 2 Solving ComplexOptimization Problems Example: Class Responsibility Assignment Problem Modularization of features into classes
- 3. Problem Statement 2 Solving ComplexOptimization Problems Example: Class Responsibility Assignment Problem Modularization of features into classes 1 2 5 15 52 203 … 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000 1,000,000,000 10,000,000,000 100,000,000,000 1,000,000,000,000 10,000,000,000,000 100,000,000,000,000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 NumberofPossibilities Number of Features
- 4. Metamodel? Problem Statement 3 Solving ComplexOptimization Problems ClassModel name : EString Feature name : EString Class name : EString features classes isEncapsulatedBy dataDep 0..* 0..* 0..* 0..* 0..1 0..* AttributeMethod encapsulates funcDep
- 5. Trans- formations? Problem Statement 3 Solving ComplexOptimization Problems Rule createClass(className: EString) «preserve» : ClassModel «forbid» : Class name = className «create» : Class name = className «create» classesclasses «forbid» Rule assignFeature(feature: Feature, class: Class) @ClassModel «preserve» feature : Feature «forbid» : Class «preserve» class : Class «create» moduleclass «forbid»
- 6. Problem Statement 3 Solving ComplexOptimization Problems Coupling? Cohesion? ?
- 7. Manually solvingproblems intelligently requires detailed knowledge Effect of transformation rules on output model Conflicting and supporting rules Rule parameterization Rule scheduling: Order of rules Conflicting output characteristics Problem Statement 3 Solving Complex Optimization Problems Rule createClass(className: EString) «preserve» : ClassModel «forbid» : Class name = className «create» : Class name = className «create» classesclasses «forbid» Rule assignFeature(feature: Feature, class: Class) @ClassModel «preserve» feature : Feature «forbid» : Class «preserve» class : Class «create» moduleclass «forbid»
- 8. Manually solvingproblems intelligently requires detailed knowledge Effect of transformation rules on output model Conflicting and supporting rules Rule parameterization Rule scheduling: Order of rules Conflicting output characteristics Complex problem with large or even infinite transformation space Automatic transformation orchestration Problem Statement 3 Solving Complex Optimization Problems Rule createClass(className: EString) «preserve» : ClassModel «forbid» : Class name = className «create» : Class name = className «create» classesclasses «forbid» Rule assignFeature(feature: Feature, class: Class) @ClassModel «preserve» feature : Feature «forbid» : Class «preserve» class : Class «create» moduleclass «forbid»
- 9.
- 10. Aim: Automatictransformation orchestration for complex problems Utilize abstraction capabilities of MDE Benefit from declarative exploration capabilities of SBO methods Search-Based Model Transformations 5 Overview
- 11. Aim: Automatictransformation orchestration for complex problems Utilize abstraction capabilities of MDE Benefit from declarative exploration capabilities of SBO methods Search-Based Exploration Example: Population-based search method Search-Based Model Transformations 5 Overview Population (size = 10)
- 12. Aim: Automatictransformation orchestration for complex problems Utilize abstraction capabilities of MDE Benefit from declarative exploration capabilities of SBO methods Search-Based Exploration Example: Population-based search method Search-Based Model Transformations 5 Overview Select k random (k = 4) = 1 = 3 = 2 = 4 Population (size = 10)
- 13. Aim: Automatictransformation orchestration for complex problems Utilize abstraction capabilities of MDE Benefit from declarative exploration capabilities of SBO methods Search-Based Exploration Example: Population-based search method Search-Based Model Transformations 5 Overview Select k random (k = 4) Choose n best (n = 2) = 1 = 3 = 2 = 4 Population (size = 10)
- 14. Aim: Automatictransformation orchestration for complex problems Utilize abstraction capabilities of MDE Benefit from declarative exploration capabilities of SBO methods Search-Based Exploration Example: Population-based search method Search-Based Model Transformations 5 Overview Select k random (k = 4) Choose n best (n = 2) Recombine = 1 = 3 = 2 = 4 Population (size = 10) Cut Point
- 15. Aim: Automatictransformation orchestration for complex problems Utilize abstraction capabilities of MDE Benefit from declarative exploration capabilities of SBO methods Search-Based Exploration Example: Population-based search method Search-Based Model Transformations 5 Overview Select k random (k = 4) Choose n best (n = 2) Recombine Mutate = 1 = 3 = 2 = 4 Population (size = 10) Cut Point
- 16. Aim: Automatictransformation orchestration for complex problems Utilize abstraction capabilities of MDE Benefit from declarative exploration capabilities of SBO methods Search-Based Exploration Example: Population-based search method Idea: Define generic encoding based on model transformations Transformation as sequence of transformation units Parameters set automatically and randomly Search-Based Model Transformations 5 Overview Select k random (k = 4) Choose n best (n = 2) Recombine Mutate = 1 = 3 = 2 = 4 Population (size = 10) Cut Point Transformation Units = Decision Variables rule = createClass name = 'Class A' rule = assignFeature class = Class A feature = Feature A Placeholder rule = createClass name = 'Class B'
- 17. Marrying Optimizationand Model Transformations Based on Eclipse platform Eclipse EMF as modeling environment Henshin as graph transformation engine MOEA Framework for SBO algorithms Search-Based Model Transformations 6 Marrying Optimization and Model Transformations (MOMoT) MOMoT Problem Instance Model Objectives Constraints Transformation Rules Exploration Configuration Result Models Objective Values Generic Encoding Search-based Exploration Result Generation Constraint Values Rule Orchestrations Exploration Statistics Objective and Constraint Evaluation Transformation Orchestration [1] MOMoT Project: http://martin-fleck.github.io/momot/ [2] Eclipse Modeling Framework: https://eclipse.org/modeling/emf/ [3] Henshin Project: https://www.eclipse.org/henshin/ [4] MOEA Framework: http://moeaframework.org/ MOEA
- 18. Search-Based Model Transformations 7 Demo Demo (linkedin next slide and available at https://www.youtube.com/watch?v=BQDz6OmxABE)
- 19. Summary Transformationorchestration is a non-trivial task Smart exploration of transformation space to solve complex problems Generic: Problem- and SBO method-agnostic approach Transparent: Input and output provided directly on model level Declarative: Objectives and constraints specified using MDE techniques Supportive: Dedicated configuration language for model engineers Search-Based Model Transformations 8 Summary MOMoT Problem Instance Model Objectives Constraints Transformation Rules Exploration Configuration Result Models Objective Values Generic Encoding Search-based Exploration Result Generation Constraint Values Rule Orchestrations Exploration Statistics Objective and Constraint Evaluation Transformation Orchestration
- 20. Interesting ResearchLines Application on out-place transformations Support of memetic algorithms Integration of optimization method languages Search-Based Model Transformations 9 Further Research MOMoT Problem Instance Model Objectives Constraints Transformation Rules Exploration Configuration Result Models Objective Values Generic Encoding Search-based Exploration Result Generation Constraint Values Rule Orchestrations Exploration Statistics Objective and Constraint Evaluation Transformation Orchestration
- 21. Search-Based Model Transformationswith MOMoT http://martin-fleck.github.io/momot/ ICMT, Vienna 4th July 2016 A Tool Demonstration Martin Fleck, Javier Troya, Manuel Wimmer
Editor's Notes
- #11 Mention Input-Output Transformation Concrete algorithms include local search methods such as Tabu Search [10] and Simulated Annealing [14], or genetic algorithms [12] such as NSGA-II [6] and NSGA-III [5]. Therefore, an alternative approach is the usage of meta-heuristics that are problem-independent. This line is investigated by Search-Based Software Engineering (SBSE) [11], which is a lively research field applying search-based optimization techniques to software engineering problems. Search-based optimization techniques deal with large or even infinite search spaces in an efficient manner.
- #12 Mention Input-Output Transformation Concrete algorithms include local search methods such as Tabu Search [10] and Simulated Annealing [14], or genetic algorithms [12] such as NSGA-II [6] and NSGA-III [5]. Therefore, an alternative approach is the usage of meta-heuristics that are problem-independent. This line is investigated by Search-Based Software Engineering (SBSE) [11], which is a lively research field applying search-based optimization techniques to software engineering problems. Search-based optimization techniques deal with large or even infinite search spaces in an efficient manner.
- #13 Mention Input-Output Transformation Concrete algorithms include local search methods such as Tabu Search [10] and Simulated Annealing [14], or genetic algorithms [12] such as NSGA-II [6] and NSGA-III [5]. Therefore, an alternative approach is the usage of meta-heuristics that are problem-independent. This line is investigated by Search-Based Software Engineering (SBSE) [11], which is a lively research field applying search-based optimization techniques to software engineering problems. Search-based optimization techniques deal with large or even infinite search spaces in an efficient manner.
- #14 Mention Input-Output Transformation Concrete algorithms include local search methods such as Tabu Search [10] and Simulated Annealing [14], or genetic algorithms [12] such as NSGA-II [6] and NSGA-III [5]. Therefore, an alternative approach is the usage of meta-heuristics that are problem-independent. This line is investigated by Search-Based Software Engineering (SBSE) [11], which is a lively research field applying search-based optimization techniques to software engineering problems. Search-based optimization techniques deal with large or even infinite search spaces in an efficient manner.
- #15 Mention Input-Output Transformation Concrete algorithms include local search methods such as Tabu Search [10] and Simulated Annealing [14], or genetic algorithms [12] such as NSGA-II [6] and NSGA-III [5]. Therefore, an alternative approach is the usage of meta-heuristics that are problem-independent. This line is investigated by Search-Based Software Engineering (SBSE) [11], which is a lively research field applying search-based optimization techniques to software engineering problems. Search-based optimization techniques deal with large or even infinite search spaces in an efficient manner.
- #16 Mention Input-Output Transformation Concrete algorithms include local search methods such as Tabu Search [10] and Simulated Annealing [14], or genetic algorithms [12] such as NSGA-II [6] and NSGA-III [5]. Therefore, an alternative approach is the usage of meta-heuristics that are problem-independent. This line is investigated by Search-Based Software Engineering (SBSE) [11], which is a lively research field applying search-based optimization techniques to software engineering problems. Search-based optimization techniques deal with large or even infinite search spaces in an efficient manner.
- #17 Mention Input-Output Transformation Concrete algorithms include local search methods such as Tabu Search [10] and Simulated Annealing [14], or genetic algorithms [12] such as NSGA-II [6] and NSGA-III [5]. Therefore, an alternative approach is the usage of meta-heuristics that are problem-independent. This line is investigated by Search-Based Software Engineering (SBSE) [11], which is a lively research field applying search-based optimization techniques to software engineering problems. Search-based optimization techniques deal with large or even infinite search spaces in an efficient manner.