bbob
A black-box optimization benchmark suite containing a variety of functions, dimensions, and budget settings, widely used to evaluate the performance of continuous optimization algorithms.
Timeline (last 90 days)
AMM conducted a pre-registered development loop on the bbob benchmark, and 32 of 36 predictions held in the subsequent 16 confirmations.
AMM was tested once on held-out bbob instances 101-110 (D=2-40, budgets 10*D to 1000*D) against up to seven competitors; across 600 function-dimension-budget cells, AMM had the highest objective share in 382 cells (236 outright, 146 tied).
Under the descriptive posterior rule, AMM credibly trailed on only 35 cells in the held-out bbob test, concentrated mainly in the low-budget regimes of 10*D and 30*D.
The pre-registered runs showed that AMM's lead over IPOP-CMA-ES holds on affine-transformed and box-constrained bbob, and also holds on the combined results for rotated f2-f4.
All Facts (4)
AMM conducted a pre-registered development loop on the bbob benchmark, and 32 of 36 predictions held in the subsequent 16 confirmations.
50%UnverifiedAMM was tested once on held-out bbob instances 101-110 (D=2-40, budgets 10*D to 1000*D) against up to seven competitors; across 600 function-dimension-budget cells, AMM had the highest objective share in 382 cells (236 outright, 146 tied).
50%UnverifiedUnder the descriptive posterior rule, AMM credibly trailed on only 35 cells in the held-out bbob test, concentrated mainly in the low-budget regimes of 10*D and 30*D.
50%UnverifiedThe pre-registered runs showed that AMM's lead over IPOP-CMA-ES holds on affine-transformed and box-constrained bbob, and also holds on the combined results for rotated f2-f4.
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