Finding Robust Solutions to Stable Marriage
Authors: Begum Genc, Mohamed Siala, Barry O'Sullivan, Gilles Simonin
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation on large instances show that local search outperforms the other approaches. |
| Researcher Affiliation | Academia | 1 Insight, Centre for Data Analytics, Department of Computer Science, University College Cork, Ireland 2 TASC, Institut Mines Telecom Atlantique, LS2N UMR 6004, Nantes, France |
| Pseudocode | No | The paper describes algorithms (Genetic Algorithm, Local Search) but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using Choco 4.0.1 and implementing metaheuristics in Java, but does not provide concrete access to the source code for the methodology described in the paper. |
| Open Datasets | No | The paper uses 'random instances' that are generated, not publicly available datasets with concrete access information (link, DOI, formal citation). |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning for training, validation, or testing. |
| Hardware Specification | Yes | All experiments were performed on DELL M600 with 2.66 Ghz processors under Linux. |
| Software Dependencies | Yes | The CP model is implemented in Choco 4.0.1 [Prud homme et al., 2016] and the two metaheuristics are implemented in Java. |
| Experiment Setup | Yes | The time limit is fixed to 20 minutes for every run. An additional cut-off is used for local search (LS) and genetic algorithm (GA) as follows: if the solution quality does not improve for 10000 iterations, we terminate the search. The genetic algorithm applies a crossover at each iteration unless the roulette wheel selection selects the fittest stable matching from the population. Additionally, the probability of applying mutation on a randomly selected stable matching is fixed as 80%. For local search, we chose to restart the local search with a randomly generated stable matching every 50 iterations. |