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Automatic Generation of Atomic Consistency Preserving Search Operators for Search-Based Model Engineering

Research output: Chapter in Book/Report/Conference proceedingConference paper

Original languageEnglish
Title of host publicationIEEE / ACM 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS)
Number of pages11
Publication statusAccepted/In press - 18 Jun 2019

Documents

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    main.pdf, 506 KB, application/pdf

    8/08/2019

    Final published version

King's Authors

Abstract

Recently there has been increased interest in combining the fields of Model-Driven Engineering (MDE) and Search-Based Software Engineering (SBSE). Such approaches use meta-heuristic search guided by search operators (model mutators and sometimes breeders) implemented as model transformations. The design of these operators can substantially impact the effectiveness and efficiency of the metaheuristic search. Currently, designing search operators is left to the person specifying the optimisation problem. However, developing
consistent and efficient search-operator rules requires not only domain expertise but also in-depth knowledge about optimisation, which makes the use of model-based meta-heuristic search challenging and expensive. In this paper, we propose a generalised approach to automatically generate atomic consistency preserving search operators (aCPSOs) for a given optimisation problem. This reduces the effort required to specify an optimisation problem and shields optimisation users from the complexity of implementing efficient meta-heuristic search mutation operators. We evaluate our approach with a set of case studies, and show that the automatically generated rules are comparable to, and in some cases better than, manually created rules at guiding evolutionary search towards near-optimal solutions.

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