Solving the TTC'16 Class Responsibility Assignment Case Study with SIGMA and Multi-Objective Genetic Algorithms

Abstract : In this paper we describe a solution for the Transformation Tool Contest 2016 (TTC'16) Class Responsibility Assignment (CRA) case study using Sigma, a family of Scala internal Domain-Specific Languages (DSLs) that provides an expressive and efficient API for model consistency checking and model transformations. Since the Class Responsibility Assignment problem is a search-based problem, we base our solution on multi-objective genetic algorithms. Concretely, we use NSGA-III and SPEA2 to minimize the coupling between classes' structural features and to maximize their cohesion.
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Filip Křikava. Solving the TTC'16 Class Responsibility Assignment Case Study with SIGMA and Multi-Objective Genetic Algorithms. Transformation Tool Contest, Jul 2016, Vienna, Austria. ⟨hal-01615255⟩

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