Theoretical Study on Multi-objective Heuristic Search
Authors: Shawn Skyler, Shahaf Shperberg, Dor Atzmon, Ariel Felner, Oren Salzman, Shao-Hung Chan, Han Zhang, Sven Koenig, William Yeoh, Carlos Hernandez Ulloa
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we study and empirically compare different ordering functions. We evaluated the expansions of each OF in the different expansion phases defined above on 200 random instances of the BAY road-map [DIMACS, 2006] with 2 4 objectives: time, distance, money, and uniform cost (respectively). |
| Researcher Affiliation | Academia | Ben-Gurion University of the Negev, Bar-Ilan University, Technion Israel Institute of Technology, University of Southern California, Washington University in St. Louis, Universidad San Sebasti an |
| Pseudocode | Yes | Algorithm 1: MOS-A |
| Open Source Code | No | No concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper was found. |
| Open Datasets | Yes | We evaluated the expansions of each OF in the different expansion phases defined above on 200 random instances of the BAY road-map [DIMACS, 2006] |
| Dataset Splits | No | The paper states '200 random instances of the BAY road-map' were used, but does not specify any training, validation, or test dataset splits, exact percentages, or sample counts for reproducibility. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running the experiments were provided in the paper. General computing environments were not specified either. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, that would be needed to replicate the experiments. |
| Experiment Setup | No | The paper describes the different Ordering Functions (OFs) and Tie-breaking Policies (TBs) that are central to its analysis, but it does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings typically found in experimental sections (e.g., learning rates, batch sizes, epochs). |