Research map / Learning theory and algorithms / Combinatorial and search optimization
On the Time Complexity of Algorithm Selection Hyper-Heuristics for Multimodal Optimisation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| allow occasional all-moves acceptance (p = 1/((1+eps)n)) instead of pure elitism (p = 0) | Move Acceptance hyper-heuristic (MAHH-OI) · CLIFF_d | expected runtime: infinite -> O(n log n + n^3/d^2) | Helped |
| use constant p instead of p <= 1/((1+eps)n) (ablation) | MAHH-OI · OneMax | expected runtime: O(n log n) -> 2^Omega(n) | Hurt |
| replace Metropolis with MAHH-OI | non-elitist search on hard CLIFF_d · CLIFF_d | expected runtime: O(n^3) worst case vs superpolynomial for Metropolis | Helped |
| replace (1+1) EA with MAHH-OI at cliff length d > 3 | elitist EA · CLIFF_d | expected runtime | Helped |
| replace Metropolis with MAHH-OI | non-elitist search · JUMP_m | expected runtime: O(n log n + n^(2m-1)/m) vs 2^Omega(n) | Helped |
| replace MAHH-OI with (1+1) EA on JUMP_m | MAHH-OI · JUMP_m | expected runtime: Theta(n^m) vs O(n log n + n^(2m-1)/m) | Helped |
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