@inproceedings{MTMT:3335161,
	title = {A Graph Solver for the Automated Generation of Consistent Domain-Specific Models},
	url = {https://m2.mtmt.hu/api/publication/3335161},
	author = {Semeráth, Oszkár and Nagy, András Szabolcs and Varró, Dániel},
	booktitle = {ICSE '18},
	doi = {10.1145/3180155.3180186},
	unique-id = {3335161},
	year = {2018},
	pages = {980-980},
	orcid-numbers = {Varró, Dániel/0000-0002-8790-252X}
}

@inproceedings{MTMT:3203907,
	title = {Effects of Graph Transformation Rules to Design Space Exploration Problems},
	url = {https://m2.mtmt.hu/api/publication/3203907},
	author = {Nagy, András Szabolcs and Varró, Dániel},
	booktitle = {Proceedings of the 24th PhD Mini-Symposium},
	doi = {10.5281/zenodo.291901},
	unique-id = {3203907},
	year = {2017},
	pages = {58-61}
}

@CONFERENCE{MTMT:3169906,
	title = {Class Responsibility Assignment Case: a VIATRA-DSE Solution},
	url = {https://m2.mtmt.hu/api/publication/3169906},
	author = {Nagy, András Szabolcs and Szárnyas, Gábor},
	booktitle = {Proceedings of the 9th Transformation Tool Contest},
	unique-id = {3169906},
	year = {2016},
	pages = {39-44}
}

@inproceedings{MTMT:3094035,
	title = {Swarm Intelligence Meets Rule-Based Design Space Exploration},
	url = {https://m2.mtmt.hu/api/publication/3094035},
	author = {Alexandra, Anna Sólyom and Nagy, András Szabolcs},
	booktitle = {Proceedings of the 23rd PhD Mini-Symposium},
	unique-id = {3094035},
	year = {2016},
	pages = {42-45}
}

@inproceedings{MTMT:2763246,
	title = {Multi-Objective Optimization in Rule-Based Design Space Exploration},
	url = {https://m2.mtmt.hu/api/publication/2763246},
	author = {Hani, Abdeen and Varró, Dániel and Houari, Sahraoui and Nagy, András Szabolcs and Debreceni, Csaba and Hegedüs, Ábel and Horváth, Ákos},
	booktitle = {Proceedings of the 29th ACM/IEEE International Conference on Automated Software Engineering},
	doi = {10.1145/2642937.2643005},
	unique-id = {2763246},
	abstract = {Design space exploration (DSE) aims to find optimal design 
		candidates of a domain with respect to different objectives 
		where design candidates are constrained by complex structural 
		and numerical restrictions. Rule-based DSE aims to find such 
		candidates that are reachable from an initial model by 
		applying a sequence of exploration rules. Solving a rule-
		based DSE problem is a difficult challenge due to the 
		inherently dynamic nature of the problem.
		
		In the current paper, we propose to integrate multi-objective 
		optimization techniques by using Non-dominated Sorting 
		Genetic Algorithms (NSGA) to drive rule-based design space 
		exploration. For this purpose, finite populations of the most 
		promising design candidates are maintained wrt. different 
		optimization criteria. In our context, individuals of a 
		generation are defined as a sequence of rule applications 
		leading from an initial model to a candidate model. 
		Populations evolve by mutation and crossover operations which 
		manipulate (change, extend or combine) rule execution 
		sequences to yield new individuals.
		
		Our multi-objective optimization approach for rule-based DSE 
		is domain independent and it is automated by tooling built on 
		the Eclipse framework. The main added value is to seamlessly 
		lift multi-objective optimization techniques to the 
		exploration process preserving both domain independence and a 
		high-level of abstraction. Design candidates will still be 
		represented as models and the evolution of these models as 
		rule execution sequences. Constraints are captured by model 
		queries while objectives can be derived both from models or 
		rule applications.},
	year = {2014},
	pages = {289-300},
	orcid-numbers = {Varró, Dániel/0000-0002-8790-252X; Debreceni, Csaba/0000-0002-6263-7758; Hegedüs, Ábel/0000-0002-8940-4348}
}