Abstract
Given that real-world multi-objective optimization problems are generally constructed by combining individual functions to be optimized, it seems sensible that benchmark functions would also follow this procedure. Since the pool of functions to choose from is large and the number of function combinations increases exponentially with the number of objectives, we need a smart way to choose a reasonably sized and diverse collection of function combinations to use in benchmarking experiments. We propose a four-step approach that analyzes the landscape characteristics of all function combinations and selects only the most diverse ones to form a suite of problems. In this initial study, we test this idea on the pool of bbob functions and the case of two objectives. We provide a proof of concept for the proposed approach and its initial results. We also discuss its limitations to be addressed in future work.
A. Andova, T. Benecke and H. Ludwig—Contributed equally to this work.
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Acknowledgements
Special thanks to the Species Society and the organizers of the Species Summer School 2022 for connecting us and making this research possible.
We acknowledge financial support from the Slovenian Research Agency (research project “Constrained multi-objective Optimization Based on Problem Landscape Analysis”, young researcher program and research core funding no. P2-0209). This work is also part of the Research Initiative “SmartProSys: Intelligent Process Systems for the Sustainable Production of Chemicals” funded by the Ministry for Science, Energy, Climate Protection and the Environment of the State of Saxony-Anhalt.
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Andova, A., Benecke, T., Ludwig, H., Tušar, T. (2023). Towards Constructing a Suite of Multi-objective Optimization Problems with Diverse Landscapes. In: Correia, J., Smith, S., Qaddoura, R. (eds) Applications of Evolutionary Computation. EvoApplications 2023. Lecture Notes in Computer Science, vol 13989. Springer, Cham. https://doi.org/10.1007/978-3-031-30229-9_29
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