The project aims the research of benchmarking techniques for derivative-free optimization methods. The two main directions in the development of these optimization methods are mathematical programming (e.g. the DIRECT method) and evolutionary algorithms (e.g. the differential evolution algorithm). The term benchmarking refers to a set of procedures for comparing such methods. Appropriately chosen benchmarking techniques can reveal the structural bias of some methods, or create guidelines for choosing suitable methods for a given optimization problem. Some of the current issues in this field are the strong emphasis on artificially created benchmark sets, the structural problems of some sets and algorithms, and the small intersection between benchmarking techniques and the comparison of methods from the two main development directions mentioned above.

Keywords
global optimization;derivative-free methods;benchmarking

24-12474S

English

Kůdela Jakub, doc. Ing., Ph.D. - principal person responsible

Institute of Automation and Computer Science
- responsible department (28.3.2023 - not assigned)
Institute of Automation and Computer Science
- beneficiary (28.3.2023 - not assigned)

MATOUŠEK, R.; HŮLKA, T.; LOZI, R.; KŮDELA, J. Semi-Stable Periodic Orbits of the Deterministic Chaotic Systems Designed by means of Genetic Programming. In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE Xplore. IEEE, 2024. ISBN: 979-8-3503-0836-5.
Detail

KŮDELA, J.; DOBROVSKÝ, L.; SHEHADEH, M.; HŮLKA, T.; MATOUŠEK, R. Comparing Surrogate-Assisted Evolutionary Algorithms on Optimization of a Simulation Model for Resource Planning Task for Hospitals. In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. ISBN: 979-8-3503-0836-5.
Detail

KŮDELA, J.; JUŘÍČEK, M.; PARÁK, R.; TZANETOS, A.; MATOUŠEK, R. Benchmarking Derivative-Free Global Optimization Methods on Variable Dimension Robotics Problems. In 2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024. ISBN: 979-8-3503-0836-5.
Detail

KŮDELA, J.; DOBROVSKÝ, L. Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems. In 18th International Conference on Parallel Problem Solving from Nature. Springer Science and Business Media Deutschland GmbH, 2024. p. 303-321. ISBN: 978-3-031-70068-2.
Detail

TZANETOS, A.; KŮDELA, J. Working on the Structural Components of Evolutionary Approaches. In Proceedings of the 16th International Joint Conference on Computational Intelligence. Science and Technology Publications, Lda, 2024. p. 375-382. ISBN: 978-989-758-721-4.
Detail

STRIPINIS, L.; KŮDELA, J.; PAULAVIČIUS, R. Hot Off the Press: Benchmarking Derivative-Free Global Optimization Algorithms under Limited Dimensions and Large Evaluation Budgets. In 2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion. Association for Computing Machinery, Inc, 2024. p. 57-58. ISBN: 979-8-4007-0495-6.
Detail

STRIPINIS, L.; KŮDELA, J.; PAULAVIČIUS, R. Benchmarking Derivative-Free Global Optimization Algorithms Under Limited Dimensions and Large Evaluation Budgets. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2025, vol. 29, no. 1, p. 187-204. ISSN: 1089-778X.
Detail

Responsibility: Kůdela Jakub, doc. Ing., Ph.D.