Towards Model-Based Optimisation: Using Domain Knowledge Explicitly

Zschaler, Steffen; Mandow, Lawrence

doi:10.1007/978-3-319-50230-4_24

Cited by 19 publications

(18 citation statements)

References 24 publications

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“…We have introduced MDEOptimiser (MDEO) previously in [2,15]. MDEO performs model-based optimisation by running evolutionary optimisation with candidate solutions represented by model instances of a given meta-model.…”

Section: Related Workmentioning

confidence: 99%

“…We have implemented our approach using our MDEO tool [2,15]. The tool is an Eclipse plugin allowing the user to specify model optimisation in the EMF context through a DSL.…”

Section: Searching Optimal Models With Generated Rulesmentioning

confidence: 99%

“…There has been a good deal of interest in optimisation of models in recent years [1,2,3,4,5,6,7,8]. These approaches aim to provide support for search-based software engineering [9] in an MDE context.…”

Section: Introductionmentioning

confidence: 99%

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Towards Automatic Generation of Evolution Rules for Model-Driven Optimisation

Burdusel

Zschaler

2018

Software Technologies: Applications and Foundations

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Over recent years, optimisation and evolutionary search have seen substantial interest in the MDE research community. Many of these techniques require the specification of an optimisation problem to include a set of model transformations for deriving new solution candidates from existing ones. For some problems-for example, planning problems, where the domain only allows specific actions to be taken-this is an appropriate form of problem specification. However, for many optimisation problems there is no such domain constraint. In these cases providing the transformation rules over-specifies the problem. The choice of rules has a substantial impact on the efficiency of the search, and may even cause the search to get stuck in local optima. In this paper, we propose a new approach to specifying optimisation problems in an MDE context without the need to explicitly specify evolution rules. Instead, we demonstrate how these rules can be automatically generated from a problem description that consists of a meta-model for problems and candidate solutions, a list of meta-classes, instances of which describe potential solutions, a set of additional multiplicity constraints to be satisfied by candidate solutions, and a number of objective functions. We show that rules generated in this way lead to optimisation runs that are at least as efficient as those using hand-written rules.1 https://eclipse.org/modeling/emf/ 2 http://moeaframework.org/

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Section: Related Workmentioning

confidence: 99%

“…We have implemented our approach using our MDEO tool [2,15]. The tool is an Eclipse plugin allowing the user to specify model optimisation in the EMF context through a DSL.…”

Section: Searching Optimal Models With Generated Rulesmentioning

confidence: 99%

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Towards Automatic Generation of Evolution Rules for Model-Driven Optimisation

Burdusel

Zschaler

2018

Software Technologies: Applications and Foundations

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“…Search-based problems have long been the subject of intense research interest [16]. More recently, there have been proposals for tools solving search problems in an MDE context [26,27,28]. Of particular interest in the context of GTS families is the work on MoMOT by Fleck et al [28].…”

Section: Finding Gts-family Membersmentioning

confidence: 99%

GTS Families for the Flexible Composition of Graph Transformation Systems

Zschaler

Durán

2017

Fundamental Approaches to Software Engineering

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Abstract. Morphisms between graph-transformation systems (GTSs) have been successfully used for the refinement, reuse, and composition of GTSs. All these uses share a fundamental problem: to be able to define a morphism, source and target GTSs need to be quite similar in their structure (in terms of both the type graphs and the set of rules and their respective structures). This limits the applicability of these approaches by excluding a wide range of mappings that would intuitively be accepted as meaningful, but that cannot be captured formally as a morphism. Some researchers have attempted to introduce some flexibility, but these attempts either focus only on the type graphs (e.g., Kleisli morphisms between type graphs) or only support specific forms of deviation (e.g., supporting sub-typing in type graphs through clan morphisms). In this work, we introduce the notion of GTS families, which provide a general mechanism for explicitly expressing the amount of acceptable adaptability of the involved GTSs so that the intended morphisms can be defined. On this basis, we demonstrate how GTS families that are extension preserving can be used to enable flexible GTS amalgamation.

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“…Recently, a variety of MDO frameworks has emerged [BFT + 17, AVS + 14, ZM16,Str17] and been applied successfully in numerous use-cases, including security-oriented software refactoring [RKL + 18], model generation [SNV18], transformation modularization [FTK + 17], and various more examples [BSAN17]. A key distinction in MDO frameworks concerns the way in which solutions are encoded [ZM16]: The model-based encoding approach represents solutions as models. In the rule-based encoding approach, a solution is a sequence of rule calls in the context of a given input model.…”

Section: Introductionmentioning

confidence: 99%

Searching for Optimal Models: Comparing Two Encoding Approaches.

John¹,

Burdusel²,

Bill³

et al. 2019

JOT

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Search-Based Software Engineering (SBSE) is about solving software development problems by formulating them as optimization problems. In the last years, combining SBSE and Model-Driven Engineering (MDE), where models and model transformations are treated as key artifacts in the development of complex systems, has become increasingly popular. While search-based techniques have often successfully been applied to tackle MDE problems, a recent line of research investigates how a model-driven design can make optimization more easily accessible to a wider audience. In previous model-driven optimization efforts, a major design decision concerns the way in which solutions are encoded. Two main options have been explored: a model-based encoding representing candidate solutions as models, and a rule-based encoding representing them as sequences of transformation rule applications. While both encodings have been applied to different use cases, no study has yet compared them systematically. To close this gap, we evaluate both approaches on a common set of optimization problems, investigating their impact on the optimization performance. Additionally, we discuss their differences, strengths, and weaknesses laying the foundation for a knowledgeable choice of the right encoding for the right problem.

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Towards Model-Based Optimisation: Using Domain Knowledge Explicitly

Cited by 19 publications

References 24 publications

Towards Automatic Generation of Evolution Rules for Model-Driven Optimisation

Towards Automatic Generation of Evolution Rules for Model-Driven Optimisation

GTS Families for the Flexible Composition of Graph Transformation Systems

Searching for Optimal Models: Comparing Two Encoding Approaches.

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