Sequence Selection by Pareto Optimization
Authors: Chao Qian, Chao Feng, Ke Tang
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results exhibit the superior performance of POSEQSEL. In this section, we investigate the empirical performance of POSEQSEL by synthetic experiments. |
| Researcher Affiliation | Academia | 1 Anhui Province Key Lab of Big Data Analysis and Application, University of Science and Technology of China, Hefei 230027, China 2 Shenzhen Key Lab of Computational Intelligence, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China |
| Pseudocode | Yes | Algorithm 1 POSEQSEL Algorithm |
| Open Source Code | No | The paper does not provide a link or explicit statement about the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper describes generating synthetic data for its experiments: "each probability pj i(sj) is randomly sampled from [0, 0.2]" and "for each item vi V , randomly select a subset... and set an edge from vi to each item... By assigning a weight to each edge...". It does not provide access information for any publicly available dataset. |
| Dataset Splits | No | The paper describes experimental setups but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers used for the experiments. |
| Experiment Setup | Yes | We set m = 50, n = 500, and each probability pj i(sj) is randomly sampled from [0, 0.2]. The number T of iterations of POSEQSEL is set to 2ek2(k + 1)n as suggested by Theorem 2. The budget k is set as {10, 12, . . . , 30}. We set n = 30 and k = 5. The number T of iterations of POSEQSEL is set to 4ek2n2 as suggested by Theorem 3. For each d {1, 2, . . . , 10}, we randomly generate 50 problem instances, and report the average results. |