Abstract
Local optima networks (LONs) are a useful tool to analyse and visualise the global structure of fitness landscapes. The main goal of our study is to use LONs to contrast the global structure of synthetic benchmark functions against those of real-world continuous optimisation problems of similar dimensions. We selected two real-world problems, namely, an engineering design problem and a machine learning problem. Our results indicate striking differences in the global structure of synthetic vs real-world problems. The real-world problems studied were easier to solve than the synthetic ones, and our analysis reveals why; they have easier to traverse global structures with fewer nodes and edges, no sub-optimal funnels, higher neutrality and multiple global optima with shorter trajectories towards them.
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Acknowledgment
Marco A. Contreras-Cruz thanks to the National Council of Science and Technology (CONACYT) for the scholarship with identification number 568675/302121. He also thanks to the Office of Research and Graduate Programs (DAIP) of the University of Guanajuato and the CONACYT, for the financial support during his research visit to the University of Stirling (from June to December 2019).
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Contreras-Cruz, M.A., Ochoa, G., Ramirez-Paredes, J.P. (2020). Synthetic vs. Real-World Continuous Landscapes: A Local Optima Networks View. In: Filipič, B., Minisci, E., Vasile, M. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2020. Lecture Notes in Computer Science(), vol 12438. Springer, Cham. https://doi.org/10.1007/978-3-030-63710-1_1
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