Integrating Pseudo-Boolean Constraint Reasoning in Multi-Objective Evolutionary Algorithms
Authors: Miguel Terra-Neves, Inês Lynce, Vasco Manquinho
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | 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 , Inˆes 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. |