CNN regressor on contour plots of probed landscapes enables per-instance algorithm selection that outperforms the single best solver and competes with feature-based baselines on BBOB benchmarks.
Using well- understood single-objective functions in multiobjective black-box op- timization test suites,
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A suite of 235 engineering-derived optimization problems is paired with a random-sampling reference metric that normalizes performance for unbiased comparison of algorithms.
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Beyond Numerical Features: CNN-Driven Algorithm Selection via Contour Plots for Continuous Black-Box Optimization
CNN regressor on contour plots of probed landscapes enables per-instance algorithm selection that outperforms the single best solver and competes with feature-based baselines on BBOB benchmarks.
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Randomness as Reference: Benchmark Metric for Optimization in Engineering
A suite of 235 engineering-derived optimization problems is paired with a random-sampling reference metric that normalizes performance for unbiased comparison of algorithms.