Titlebook: Evolutionary Multi-Criterion Optimization; 11th International C Hisao Ishibuchi,Qingfu Zhang,Aimin Zhou Conference proceedings 2021 Springe

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https://doi.org/10.1007/978-3-030-72062-9artificial intelligence; correlation analysis; evolutionary algorithms; evolutionary multiobjective opt

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https://doi.org/10.1007/978-94-010-3302-2an severely degrade the performance of many multi-objective evolutionary algorithms (MOEAs). In previous work, some coping strategies (e.g., the .-dominance and the modified objective calculation) have been demonstrated to be effective in eliminating DRSs. However, these strategies may in turn cause

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https://doi.org/10.1007/978-3-662-41468-2ermine a proper ranking thereof. Multiple performance indicators, e.g., the generational distance and the hypervolume, are frequently applied when reporting the experimental data, where typically the data on each indicator is analyzed independently from other indicators. Such a treatment brings conc

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Janet Becker Rodgers,Merel Ritskes-Hoitingae selection method for objective reduction. In our proposed method, each objective is formulated as a positive linear combination of a small number of essential objectives, and sparse regularization is employed to identify redundant objectives. Our numerical experiment shows the effectiveness and ro

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https://doi.org/10.1007/978-0-387-33893-4mainly by its selection operators and . introduced mainly by its variation (crossover and mutation) operators. An attempt to improve an EA’s performance by simply adding a new and apparently promising operator may turn counter-productive, as it may trigger an imbalance between the exploitation-explo