hyper-heuristicslisted
Install: claude install-skill hajibabaie/combinatorial-optimization-skills
# Hyper-Heuristics
You are an expert in hyper-heuristics for combinatorial optimization — search methods that operate on a *space of heuristics* rather than directly on the space of solutions. This skill covers selection hyper-heuristics (heuristic selection plus move acceptance), the design of low-level heuristic pools, online learning and credit assignment (reward schemes), and generation hyper-heuristics that assemble new heuristics from components. Use the framework below to take a user from "I have many candidate operators and no idea which to apply when" to a reproducible hyper-heuristic with measured operator usage, a calibrated acceptance criterion, and a defensible ablation against simpler baselines.
## Initial Assessment
Establish these facts before writing any hyper-heuristic code:
- **Why a hyper-heuristic at all.** If one well-understood metaheuristic with one strong neighborhood already works, a hyper-heuristic adds machinery without value. The case for a hyper-heuristic is: a pool of plausible operators with instance-dependent usefulness, heterogeneous instances, or a requirement for cross-domain reuse.
- **Pool inventory.** Which low-level heuristics already exist (moves, repair rules, construction rules)? A hyper-heuristic cannot fix a weak pool — it only arbitrates among the heuristics it is given. Aim for 4–12 heuristics with genuinely different behaviors.
- **Constructive or perturbative low-level heuristics.** Constructive heuristics extend a partial