GeoPAS uses multi-scale 2D geometric slices of optimization landscapes with validity-mask pooling and a learned-plus-prior composite score to select from 12 solvers, cutting mean relative expected running time from 30.37 to around 3.1-3.6 on within-suite benchmarks.
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2026 2verdicts
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Parameterized bijective transformations on the search space of multi-objective test functions create new benchmark variants that preserve Pareto structure and measurably alter algorithm performance.
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GeoPAS: Geometric Probing for Algorithm Selection in Continuous Black-Box Optimization
GeoPAS uses multi-scale 2D geometric slices of optimization landscapes with validity-mask pooling and a learned-plus-prior composite score to select from 12 solvers, cutting mean relative expected running time from 30.37 to around 3.1-3.6 on within-suite benchmarks.
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Exploration of Pareto-preserving Search Space Transformations in Multi-objective Test Functions
Parameterized bijective transformations on the search space of multi-objective test functions create new benchmark variants that preserve Pareto structure and measurably alter algorithm performance.