Memetic Algorithm Pseudocode Demonstration

Memetic algorithms represent a powerful cultural evolution methodology that combines population-based optimization with local search techniques. This implementation demonstrates how memetic algorithms simulate cultural transmission and genetic evolution processes to efficiently locate optimal solutions for complex problems. The algorithm framework can be applied to various optimization challenges including pathfinding, parameter tuning, and function minimization. Key implementation aspects include: - Population initialization with diverse cultural traits - Genetic operators (crossover/mutation) for global exploration - Local search refinement for solution improvement - Fitness evaluation using the Rosenbrock function as the primary benchmark Developers can integrate additional test functions (such as Sphere, Rastrigin, or Ackley functions) to validate algorithm robustness and adaptability across different problem domains. While this particular implementation builds upon established research, it serves as a valuable knowledge-sharing resource for researchers and developers working in optimization algorithms, facilitating collaborative advancement across multiple disciplines.