Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations
Authors: Mina Konakovic Lukovic, Yunsheng Tian, Wojciech Matusik
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on both synthetic test functions and real-world benchmark problems show that our algorithm predominantly outperforms relevant state-of-the-art methods. |
| Researcher Affiliation | Academia | Mina Konakovi c Lukovi c MIT CSAIL minakl@mit.edu Yunsheng Tian MIT CSAIL yunsheng@csail.mit.edu Wojciech Matusik MIT CSAIL wojciech@csail.mit.edu |
| Pseudocode | Yes | Algorithm 1 DGEMO ... Algorithm 2 Batch Selection Algorithm |
| Open Source Code | Yes | The code is available at https://github.com/yunshengtian/DGEMO. ... Our code will be released open-source with reproducibility guarantee. |
| Open Datasets | Yes | First, we conduct experiments on 13 synthetic multi-objective test functions including ZDT1-3 [54], DTLZ1-6 [10], OKA1-2 [34], VLMOP2-3 [48], which are widely used in previous literature. ... Second, we adopt 7 real-world engineering design problems presented in RE problem suite [46], which are: four bar truss design, reinforced concrete beam design, hatch cover design, welded beam design, disc brake design, gear train design, and rocket injector design. |
| Dataset Splits | No | The paper mentions 'initial samples' and 'batch size' for its Bayesian optimization approach ('50 initial samples', 'batch of 10 samples'), but it does not specify traditional train/validation/test dataset splits with percentages or sample counts for the benchmark problems. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud computing instance types used for running the experiments. |
| Software Dependencies | No | The paper mentions its implementation is 'built upon pymoo [5], a state-of-the-art Python framework', but it does not specify version numbers for Python, pymoo, or any other software dependencies. |
| Experiment Setup | Yes | For every algorithm, we run every experiment with 10 different random seeds and the same 50 initial samples. In these experiments, we use a batch of 10 samples in each iteration and ran 20 iterations in total. All hyperparameters used are presented in Appendix B. |