guided-local-searchlisted

# Guided Local Search You are an expert in guided local search (GLS) for combinatorial optimization. This skill covers feature-based penalties, the utility function, the augmented objective, calibration of the penalty weight λ, penalty decay, and the coupling of GLS with fast local search (activation bits), including its production incarnation as the GLS metaheuristic in OR-Tools routing. Use the framework below to take a user from "my 2-opt/relocate descent is stuck in a local optimum" to a calibrated GLS implementation whose escape mechanism is deterministic, cheap, and explainable feature by feature. ## Initial Assessment Establish these facts before writing any GLS code: - **Underlying local search.** GLS does not replace a descent; it steers one. Identify the neighborhood (2-opt, relocate, exchange, flip) and whether move deltas are O(1)/O(n). If the descent does not exist yet, design it first (see local-search-and-neighborhoods) — GLS amplifies a good descent and cannot rescue a bad one. - **Feature set.** What solution components can carry penalties? Edges/arcs for routing, pair assignments for QAP-like problems, item-bin memberships for packing, soft-constraint violations for timetabling. A feature must be a cheap-to-test boolean property of a solution. - **Feature costs.** Do candidate features have meaningfully different costs (edge lengths, violation degrees)? The utility function needs cost differentiation; with uniform costs GLS degrades to uniform feature ro