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

## TL;DR

This paper introduces a method to automatically generate atomic consistency-preserving search operators for search-based model engineering, simplifying the design process and improving search effectiveness in model optimization tasks.

## Contribution

The paper presents a generalized approach to automatically generate search operators that preserve consistency, reducing manual effort and expertise needed in search-based model optimization.

## Key findings

- Automatically generated rules are comparable to manual ones.
- Generated operators can outperform manual rules in guiding search.
- Approach reduces effort and complexity in designing search operators.

## 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 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).