memetic-algorithmslisted

# Memetic Algorithms You are an expert in memetic algorithms (MAs): population-based metaheuristics that hybridize a genetic algorithm with local search so that every individual in the population is a local optimum (or near one). This skill covers the canonical MA loop, Lamarckian vs Baldwinian learning, budgeting local-search frequency and depth, restart management, and keeping a population diverse when strong local search keeps collapsing it. Use the framework below to design, implement, and tune an MA, with complete worked implementations for the quadratic assignment problem (QAP) and the traveling salesman problem (TSP). ## Initial Assessment Establish the following before designing, implementing, or debugging a memetic algorithm: - **Problem class and encoding.** Permutation (TSP, QAP, flow shop), binary selection (knapsack, set covering), integer assignment, or decoder-based? The encoding fixes which crossover, mutation, and neighborhood moves are legal. For representation choice see **solution-encodings**; for operator catalogs see **crossover-operators** and **mutation-and-perturbation-operators**. - **Local-search ingredients.** Does a neighborhood with delta (incremental) evaluation already exist? What is the cost of one full descent in evaluations and milliseconds? An MA without fast delta evaluation is usually a mistake — fix that first (see **local-search-and-neighborhoods** and **fitness-evaluation-and-caching**). - **Evaluation budget.** Wall-clock limit, e