3 research outputs found
Automatic Generation of Atomic Consistency Preserving Search Operators for Search-Based Model Engineering
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 meta-heuristic 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. This paper is an extended version of the paper
with the same title published in the proceedings of the 22nd International
Conference on Model Driven Engineering Languages and Systems (MODELS '19).Comment: Technical report version of the MODELS 2019 paper with the same titl