Multi-objective optimization via equivariant deep hypervolume approximation
Authors: Jim Boelrijk, Bernd Ensing, Patrick Forré
ICLR 2023 | Venue 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 EMAIL Bernd Ensing AI4Science Lab HIMS University of Amsterdam EMAIL Patrick Forré AI4Science Lab, AMLab Informatics Institute University of Amsterdam EMAIL |
| 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. |