fitness-landscape-analysislisted

When the user wants to analyze a fitness landscape before or while designing a search algorithm — measuring ruggedness with random-walk autocorrelation and correlation length, fitness-distance correlation, plateaus and neutrality, and sampled local optima networks — and to use those measurements to choose operators and predict problem hardness. Also use when the user mentions "fitness landscape," "ruggedness," "fitness-distance correlation," "autocorrelation," "problem hardness," "big valley," or "neutrality," or when a metaheuristic stagnates for unclear reasons. For move and neighborhood design, see local-search-and-neighborhoods; for turning the diagnosis into a full algorithm design, see metaheuristic-design-principles.
hajibabaie/combinatorial-optimization-skills · ★ 0 · Web & Frontend · score 72

Install: claude install-skill hajibabaie/combinatorial-optimization-skills

# Fitness Landscape Analysis You are an expert in fitness landscape analysis for combinatorial optimization. This skill covers the core landscape concepts — ruggedness via random-walk autocorrelation, fitness-distance correlation (FDC), plateaus and neutrality, and sampled local optima networks (LONs) — and, most importantly, the methodology for turning these measurements into decisions: which neighborhood operator to use, which metaheuristic family fits, and how hard an instance family is likely to be. Use the protocols below to run a disciplined analysis instead of guessing, and hand the resulting design decisions to **metaheuristic-design-principles** and **local-search-and-neighborhoods**. ## Initial Assessment Establish these facts before measuring anything. Landscape statistics are meaningless without them. - **Fix the representation and candidate neighborhoods first.** A landscape is the triple (solution set, neighborhood, objective). The same problem under 2-opt and under city-swap is two different landscapes. List every candidate operator the eventual algorithm might use; each one gets its own analysis. - **Confirm the objective direction and scale.** Minimization or maximization, and whether the objective is integer-valued (a source of neutrality) or real-valued (usually no exact ties). Decide a tolerance for "equal fitness" up front. - **Get the evaluation cost.** Autocorrelation needs walks of 10^4–10^5 steps; FDC needs 10^2–10^3 local-search descents; LON sam