%0 Conference Paper %1 10371833 %A Werner, Daniel %A Turna, Fatma %A Le, Hoang Thanh %A Middendorf, Martin %B 2023 IEEE Symposium Series on Computational Intelligence (SSCI) %D 2023 %K imported %P 1183-1188 %R 10.1109/SSCI52147.2023.10371833 %T XF-OPT/META: A Hyperparameter Optimization Framework Applied to the H -SPPBO Metaheuristic for the Dynamic TSP %X This paper has two objectives. Firstly, to introduce a new framework XF -OPTIMETA for testing and comparing Hyperparameter Optimization (HPO) methods. The framework supports model-free methods, e.g., Random Search (RS), as well as model-based methods, such as Bayesian Optimization (BO), with various surrogate models. Due to the generalized and modular structure of the XF-OPTIMETA framework, it can be easily extended to other optimization methods for dif-ferent optimization problems. The second objective is to empir-ically compare the performance of various HPO methods for population-based metaheuristics. For that the XF -OPTIMETA framework is used to apply HPO methods to the Hierarchical Simple Probabilistic Population-Based Optimization (H-SPPBO) metaheuristic for the Dynamic Traveling Salesperson Problem (DTSP) and to calculate high-performing parameter values for H-SPPBO. Promising results are obtained using the parameter values found by BO. In particular, a parameter set obtained with Gradient-Boosted Regression Trees (GBRT) outperforms a reference parameter set for H-SPPBO from an existing study.

@inproceedings{10371833, abstract = {This paper has two objectives. Firstly, to introduce a new framework XF -OPTIMETA for testing and comparing Hyperparameter Optimization (HPO) methods. The framework supports model-free methods, e.g., Random Search (RS), as well as model-based methods, such as Bayesian Optimization (BO), with various surrogate models. Due to the generalized and modular structure of the XF-OPTIMETA framework, it can be easily extended to other optimization methods for dif-ferent optimization problems. The second objective is to empir-ically compare the performance of various HPO methods for population-based metaheuristics. For that the XF -OPTIMETA framework is used to apply HPO methods to the Hierarchical Simple Probabilistic Population-Based Optimization (H-SPPBO) metaheuristic for the Dynamic Traveling Salesperson Problem (DTSP) and to calculate high-performing parameter values for H-SPPBO. Promising results are obtained using the parameter values found by BO. In particular, a parameter set obtained with Gradient-Boosted Regression Trees (GBRT) outperforms a reference parameter set for H-SPPBO from an existing study.}, added-at = {2024-12-10T12:41:16.000+0100}, author = {Werner, Daniel and Turna, Fatma and Le, Hoang Thanh and Middendorf, Martin}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/21a6df07c19d8888dba39e2f01ea07847/scadsfct}, booktitle = {2023 IEEE Symposium Series on Computational Intelligence (SSCI)}, doi = {10.1109/SSCI52147.2023.10371833}, interhash = {05f9d212c441361243b355845db9b4e6}, intrahash = {1a6df07c19d8888dba39e2f01ea07847}, issn = {2472-8322}, keywords = {imported}, month = dec, pages = {1183-1188}, timestamp = {2024-12-10T12:41:16.000+0100}, title = {XF-OPT/META: A Hyperparameter Optimization Framework Applied to the H -SPPBO Metaheuristic for the Dynamic TSP}, year = 2023 }