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Towards Automatic Generation of Evolution Rules for Model-Driven Optimisation

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Software Technologies: Applications and Foundations (STAF 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10748))

Abstract

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.

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Notes

  1. 1.

    https://eclipse.org/modeling/emf/.

  2. 2.

    http://moeaframework.org/.

  3. 3.

    This problem case also required all classes to have unique names. Given that this can be achieved by a simple post-processing step [15], we ignore the requirement for this paper.

  4. 4.

    As registered in the underlying instance of the MOEA Framework.

  5. 5.

    https://github.com/mde-optimiser/gcm-2017-experiments.

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Authors and Affiliations

  1. Department of Informatics, King’s College London, London, WC2B 4BG, UK

    Alexandru Burdusel & Steffen Zschaler

Authors
  1. Alexandru Burdusel
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  2. Steffen Zschaler
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Correspondence to Alexandru Burdusel .

Editor information

Editors and Affiliations

  1. Johannes Kepler University of Linz, Linz, Austria

    Martina Seidl

  2. Department of Informatics, King’s College London, London, United Kingdom

    Steffen Zschaler

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Burdusel, A., Zschaler, S. (2018). Towards Automatic Generation of Evolution Rules for Model-Driven Optimisation. In: Seidl, M., Zschaler, S. (eds) Software Technologies: Applications and Foundations. STAF 2017. Lecture Notes in Computer Science(), vol 10748. Springer, Cham. https://doi.org/10.1007/978-3-319-74730-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-74730-9_6

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  • Online ISBN: 978-3-319-74730-9

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