Multi-objective optimization via equivariant deep hypervolume approximation
Authors: Jim Boelrijk, Bernd Ensing, Patrick Forré
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method against exact, and approximate hypervolume methods in terms of accuracy, computation time, and generalization. We also apply and compare our methods to state-of-the-art multi-objective BO methods and EAs on a range of synthetic and real-world benchmark test cases. |
| Researcher Affiliation | Academia | Jim Boelrijk AI4Science Lab, AMLab Informatics Institute, HIMS University of Amsterdam j.h.m.boelrijk@uva.nl Bernd Ensing AI4Science Lab HIMS University of Amsterdam b.ensing@uva.nl Patrick Forré AI4Science Lab, AMLab Informatics Institute University of Amsterdam p.d.forre@uva.nl |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. The methods are described in textual form. |
| Open Source Code | Yes | Code, models, and datasets used in this work can be found at: https://github.com/ Jimbo994/deephv-iclr. |
| Open Datasets | Yes | Code, models, and datasets used in this work can be found at: https://github.com/ Jimbo994/deephv-iclr. We split our datasets into 800K training points and 100K validation and test points, respectively. |
| Dataset Splits | Yes | We split our datasets into 800K training points and 100K validation and test points, respectively. |
| Hardware Specification | Yes | All computations shown in Fig. 2 were performed on an Intel(R) Xeon(R) CPU E5-2640 CPU v4. and in the case of the GPU calculations on a NVIDIA TITAN X. |
| Software Dependencies | No | The paper mentions software like Pymoo and Bo Torch but does not provide specific version numbers for these or other key software components used in the experiments. |
| Experiment Setup | Yes | All Deep HV models have been trained with a learning rate of 10 5, using Adam and the Mean Absolute Percentage Error (MAPE) loss function (de Myttenaere et al., 2016). For the separate models, we use a batch size of 64 and train for 200 epochs. ... For the models trained on all objective cases simultaneously, we train for 100 epochs with a batch size of 128. |