Automatic generation of atomic multiplicity-preserving search operators for search-based model engineering

Abstract

Recently, there has been increased interest in combining model-driven engineering and search-based software engineering. 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 multiplicity-preserving search operators 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.

A graph-based framework for model-driven optimization facilitating impact analysis of mutation operator properties

“…Since our framework follows the model-based approach to MDO, we consider related work in more detail. We selected the following papers on the model-based approach to MDO that have been published in journals, at conferences, and at workshops on modeling and SBSE: [15,17,18,37,38,40,59,66]. We investigate which core concepts of MDO were considered.…”

“…Almost all papers on model-based MDO propose to define mutations of solution models as model transformations. Later works such as [15,38,59,66] go into more details and define as well as evaluate concrete mutation operators. Models are selected according to how well they meet the objectives or by giving preference to feasible solutions [15,38,59].…”

“…Furthermore, too many of the possible mutations can lead to infeasible solutions, so that it may be advantageous if the given set of operators is sound in the sense that mutating a feasible solution yields a feasible solution again. In [15,38,59], several sets of mutation operators were evaluated for their effectiveness, i.e., ability to produce good results, and for their efficiency, i.e., low computational cost. In particular, the results in [15] suggest that feasibility-preserving (i.e., sound) mutation operators can be advantageous.…”

“…In [15,38,59], several sets of mutation operators were evaluated for their effectiveness, i.e., ability to produce good results, and for their efficiency, i.e., low computational cost. In particular, the results in [15] suggest that feasibility-preserving (i.e., sound) mutation operators can be advantageous. To clarify what soundness and completeness can mean in the first place, and what implications they can have for evolutionary computation, we need a formal basis.…”

“…We use the framework to clarify all critical factors for conducting a reproducible experiment. In the experiment conducted, we stick to three state-of-the-art evolutionary algorithms (NSGA-II [24], PESA-II [21], and SPEA2 [64]) and we study different sets of mutation operators for three optimization problems: the Class Responsibility Assignment problem (CRA case) [13,15,32,38,47], the problem of Scrum Planning [15], and the Next Release Problem [8,15]. The experiment is based on the tools MDEOptimiser [15] and Henshin [4].…”