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Journal Articles IEEE Transactions on Evolutionary Computation Year : 2020

Landscape-aware performance prediction for evolutionary multi-objective optimization


We expose and contrast the impact of landscape characteristics on the performance of search heuristics for black-box multi-objective combinatorial optimization problems. A sound and concise summary of features characterizing the structure of an arbitrary problem instance is identified and related to the expected performance of global and local dominance-based multi-objective optimization algorithms. We provide a critical review of existing features tailored to multi-objective combinatorial optimization problems, and we propose additional ones that do not require any global knowledge from the landscape, making them suitable for large-size problem instances. Their intercorrelation and their association with algorithm performance are also analyzed. This allows us to assess the individual and the joint effect of problem features on algorithm performance, and to highlight the main difficulties encountered by such search heuristics. By providing effective tools for multi-objective landscape analysis, we highlight that multiple features are required to capture problem difficulty, and we provide further insights into the importance of ruggedness and multimodality to characterize multi-objective combinatorial landscapes.
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Dates and versions

hal-02294201 , version 1 (02-12-2020)



Arnaud Liefooghe, Fabio Daolio, Sébastien Verel, Bilel Derbel, Hernan Aguirre, et al.. Landscape-aware performance prediction for evolutionary multi-objective optimization. IEEE Transactions on Evolutionary Computation, 2020, 24 (6), pp.1063-1077. ⟨10.1109/TEVC.2019.2940828⟩. ⟨hal-02294201⟩
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