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..
Enhancing Constraint-Based Multi-Objective Combinatorial Optimization
Authors: Miguel Terra-Neves, Inês Lynce, Vasco Manquinho
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, experimental results on the Virtual Machine Consolidation (VMC) problem show the effectiveness of the proposed techniques. In this section, the performance of the techniques proposed in sections 3 and 4 is evaluated on instances of the Virtual Machine Consolidation (VMC) problem. |
| Researcher Affiliation | Academia | Miguel Terra-Neves, Inês Lynce, Vasco Manquinho INESC-ID / Instituto Superior Técnico, Universidade de Lisboa, Portugal |
| Pseudocode | Yes | Algorithm 1: CLD algorithm for computing an MCS; Algorithm 2: Sample MCS algorithm for sampling a single MCS of a MOCO instance; Algorithm 3: MCSEnum PD algorithm for enumerating MCSs of a MOCO instance using path diversification |
| Open Source Code | Yes | The benchmark set and the prototype that implements the algorithms evaluated in this paper are publicly available online2. (2http://sat.inesc-id.pt/dome) |
| Open Datasets | Yes | The evaluation is performed on the VMC benchmarks used in the work of Terra-Neves, Lynce, and Man quinho(2017), which are based on subsets of workload traces randomly selected from the Google Cluster Data project1. (1http://code.google.com/p/googleclusterdata/) |
| Dataset Splits | No | The paper describes the benchmark instances and their characteristics, but it does not specify explicit train/validation/test dataset splits, percentages, or methodology for partitioning the data into these sets. |
| Hardware Specification | Yes | The evaluation was conducted on an AMD Opteron 6376 (2.3 GHz) with 128 GB of RAM. |
| Software Dependencies | Yes | All algorithms were implemented in Java and Sat4j-PB (Le Berre and Parrain 2010) (version 2.3.4) was used as the PBS solver. |
| Experiment Setup | Yes | Each algorithm was executed with a memory limit of 4 GB and a time limit of 1800 seconds. Randomized algorithms were executed with 10 different seeds for each instance, and the analysis is performed using the median values over all executions. The m (n P ) parameter of Sample MCS (MCSEnum PD) was set to 1 (4), as suggested by our empirical evaluation. MGGA was adapted to consider migration costs instead and was configured to use a population size of 12, and crossover rate and mutation rate as suggested by Xu and Fortes(2010). |