A tailored SHAP-based analysis of rmnk-landscapes shows that the ruggedness parameter k drives multi-objective algorithm performance and that PLOS-net, C-PLOS-net, and funnel feature sets are complementary.
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Customized Exploration of Landscape Features Driving Multi-Objective Combinatorial Optimization Performance
A tailored SHAP-based analysis of rmnk-landscapes shows that the ruggedness parameter k drives multi-objective algorithm performance and that PLOS-net, C-PLOS-net, and funnel feature sets are complementary.