Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Integrating Pseudo-Boolean Constraint Reasoning in Multi-Objective Evolutionary Algorithms
Authors: Miguel Terra-Neves, Inรชs Lynce, Vasco Manquinho
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 5 presents an extensive experimental evaluation of the proposed techniques and the paper concludes in Section 6. Experimental results clearly show that the integration of these operators greatly improves multi-objective evolutionary algorithms MOEA/D and NSGAII. |
| Researcher Affiliation | Collaboration | Miguel Terra-Neves1,2 , Inหes Lynce1 and Vasco Manquinho1 1 INESC-ID / Instituto Superior T ecnico, Universidade de Lisboa, Portugal 2 Out Systems, Portugal |
| Pseudocode | Yes | Algorithm 1 Typical MOEA framework for MOCO; Algorithm 2 Generalized smart mutation; Algorithm 3 Smart improvement |
| Open Source Code | Yes | The smart operators were implemented in the VMAlloc solver3, a collection of algorithms for solving instances of the VMC problem which includes implementations of MOEA/D [Zhang and Li, 2007], NSGAII [Deb et al., 2000] and SCLD. ... 3https://github.com/Miguel Terra Neves/VMAlloc |
| Open Datasets | Yes | We consider the benchmark set publicly available on the DOME project website2. ... 2http://sat.inesc-id.pt/dome |
| Dataset Splits | No | The paper does not provide explicit details about the specific training, validation, and test splits used for the VMC instances, such as percentages or sample counts. |
| Hardware Specification | Yes | The evaluation was conducted on an AMD Opteron 6376 (2.3 GHz) with 128 GB of RAM, running Debian jessie. |
| Software Dependencies | No | The paper mentions 'Sat4j' as the PBS solver and provides a reference date (27 Jan. 2019) for its Gitlab repository, but it does not specify a precise version number for the software dependency. |
| Experiment Setup | Yes | All algorithms were configured as suggested in the literature [Terra-Neves et al., 2017; Terra-Neves et al., 2018]. Smart mutation was applied to each offspring produced by the regular genetic operators with probability psmr = 0.01. If the offspring was already feasible, smart improvement was used instead with relaxation rate prr = 0.2. Conflict budgets were set as follows: bsm = 20000 and bsi = 500000. |