This repository implements a Memetic Algorithm that combines a Genetic Algorithm with local search techniques to improve solution quality in combinatorial optimization problems.
The algorithm uses a Genetic Algorithm as a global search mechanism and applies local improvement procedures to selected individuals, enhancing convergence and solution refinement.
- Population-based Genetic Algorithm framework
- Selection, crossover, and mutation operators
- Elitism for preserving high-quality solutions
- Embedded local search applied to individuals
- Parameterized execution and batch evaluation on multiple instances
- Hybrid optimization (global + local search)
- Exploration vs. exploitation balance
- Improved convergence through local refinement
- C++
- Filesystem-based instance handling
- Randomized and heuristic optimization techniques
This project was developed for academic study of advanced evolutionary and hybrid optimization algorithms.