A Generic Construction for Crossovers of Graph-Like Structures

Taentzer, Gabriele; John, Stefan; Kosiol, Jens

doi:10.1007/978-3-031-09843-7_6

Cited by 2 publications

(1 citation statement)

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“…In addition, it is interesting to consider crossover (or breeding) operators for MDO and investigate the effects of soundness and completeness for these as well. In [60], we have taken a first step in this direction by defining a crossover operator for graph-like structures. It forms the formal basis for a crossover operator for EMF models; we present a first draft and implementation in [39].…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

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Optimization problems in software engineering typically deal with structures as they occur in the design and maintenance of software systems. In model-driven optimization (MDO), domain-specific models are used to represent these structures while evolutionary algorithms are often used to solve optimization problems. However, designing appropriate models and evolutionary algorithms to represent and evolve structures is not always straightforward. Domain experts often need deep knowledge of how to configure an evolutionary algorithm. This makes the use of model-driven meta-heuristic search difficult and expensive. We present a graph-based framework for MDO that identifies and clarifies core concepts and relies on mutation operators to specify evolutionary change. This framework is intended to help domain experts develop and study evolutionary algorithms based on domain-specific models and operators. In addition, it can help in clarifying the critical factors for conducting reproducible experiments in MDO. Based on the framework, we are able to take a first step toward identifying and studying important properties of evolutionary operators in the context of MDO. As a showcase, we investigate the impact of soundness and completeness at the level of mutation operator sets on the effectiveness and efficiency of evolutionary algorithms.

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Section: Discussionmentioning

confidence: 99%