logseq/pages/PAPERS___MOMOT-Chain.md

type:: JournalPaper external-links:: MOMOT Chaining 2022 - Online LaTeX Editor Overleaf full-title:: Multi-Objective Model Transformation Chain Exploration with MOMoT venue:: IST todoist:: https://todoist.com/showTask?id=5593874054 status:: ACCEPTED year:: 2024 leader:: people/apurv date-submitted:: 22-12-2023

- ![DECISION - ELSEVIER IST_ Your Submission - INFSOF-D-23-00955.pdf](../assets/DECISION_-_ELSEVIER_IST_Your_Submission_-_INFSOF-D-23-00955_1709279108637_0.pdf)
	- **Presentation improvements**
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			- *Proposed solution by Reviewer*
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			- The reviewer would like to see to what extent each objective contribute to the final result. Can be this done in general, or case by case? I guess the second, so maybe for each final result, we could try to remove the single objective individually and show the distance between the obtained new result and the optimal one (which was obtained by considering all the objectives). It is necessary to define a kind of distance metrics to compare solutions.
			- Concerning FACTOR ANALYSIS have a look at the notes from Alfonso [(1) Factorial Analysis (notion.so)](https://www.notion.so/apierantonio/Factorial-Analysis-4cf2a0af574a4551b48c30adc85fcbe6)
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	- **Relation of the proposed solution with respect to genetic algorithms**
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			- *Proposed solution*
				- We could explicitly mention that we are not using genetic algorithms. We are doing a full search space by employing heuristics to reduce the search space. Employing genetic algorithms can be a future work (see ((65e18bf2-681f-431a-98f5-bed074f14518)) )
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	- **Comparison with our TSE paper**
		- It ((65e18a4b-2ec0-44ef-b8e9-96f183405d4a))
			- What does it mean? I think we considered the TSE paper as baseline, isn't it?
				- We need to show that the obtained results confirm those in TSE paper
				- We then need to show that we can cover additional objectives that were not included by the TSE paper (in that approach, the selection approach is embedded and cannot be customized)
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	- **FUTURE WORK**
		- ((65e18ac4-2855-46c1-8a3a-976cbfe3ab93))
		- Comparison with genetic algorithm based solutions?
		  id:: 65e18bf2-681f-431a-98f5-bed074f14518
- ### TASKS
	- {{query (and [[PAPERS/MOMOT-Chain]] (task TODO DOING) (not [[GOALS-TODOIST]]))}}