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)

ESPESETH, A.; JUŘÍČEK, M.; LUDWIG, H.; TUŠAR, T. Hybrid Optimization of Horizontal Alignments in European Terrains: A Comparative Study. In Lecture Notes in Computer Science. Lecture Notes in Computer Science. Springer Science and Business Media Deutschland GmbH, 2025. no. 15612 LNCS, p. 119-136. ISBN: 9783031900617.
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IBEHEJ, D.; KŮDELA, J. Benchmarking Seven Multi-objective Optimization Methods from the PlatEMO Platform on the bbob-biobj Test Suite. In Gecco 2025 Companion Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2025. p. 1883-1890. ISBN: 9798400714641.
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IBEHEJ, D.; TZANETOS, A.; JUŘÍČEK, M.; KŮDELA, J. An Investigation of Inherent Structural Bias in Established Benchmark Sets. In Gecco 2025 Companion Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery, Inc, 2025. p. 123-126. ISBN: 9798400714641.
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IBEHEJ, D.; KŮDELA, J. An Investigation of Structural Bias in Particle Swarm Optimization. In Lecture Notes in Computer Science. Lecture Notes in Computer Science. Springer Science and Business Media Deutschland GmbH, 2025. p. 129-144. ISBN: 9783031900648.
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MATOUŠEK, R.; HŮLKA, T.; DOBROVSKÝ, L.; KOŘENEK, M. Synergistic Hybridization of GP and DE: Innovations in Evolutionary Computation. In Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York, NY, United States: Association for Computing Machinery, 2025. p. 639-642. ISBN: 979-8-4007-1464-1.
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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.
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SHEHADEH, M.; KŮDELA, J. Benchmarking global optimization techniques for unmanned aerial vehicle path planning. Expert Systems with Applications, 2025, vol. 293, no. Dec, p. 1-19. ISSN: 0957-4174.
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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. 8 p. ISBN: 979-8-3503-0836-5.
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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.
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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.
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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.
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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. 8 p. ISBN: 979-8-3503-0836-5.
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HŮLKA, T.; MATOUŠEK, R.; DOBROVSKÝ, L.; KŮDELA, J.; HOJNY, O. Cartesian Genetic Programming with a Modified Selection Operator for Combinational Circuit Design: Arithmetic Multipliers and Adders. In Lecture Notes in Artificial Intelligence. Lecture Notes in Computer Science. CHAM: Springer Nature, 2025. p. 53-65. ISBN: 978-3-031-84355-6.
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RUSIN, F.; FIALA, J.; SANKER, J.; BHATTARAI, S.; EKÁRT, A. The Importance of Being Earnest: Multiple Heterogeneous Container Loading with a Simple Genetic Algorithm. In Lecture Notes in Computer Science. Lecture Notes in Computer Science. Springer Science and Business Media Deutschland GmbH, 2025. no. 15612 LNCS, p. 482-495. ISBN: 9783031900617.
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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. 7 p. ISBN: 979-8-3503-0836-5.
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Responsibility: Kůdela Jakub, doc. Ing., Ph.D.