We are excited to share our latest research paper, “Harnessing Memetic Algorithms: A Practical Guide”, has just been published in Springer’s TOP journal. This work aims to provide a clear and practical guide to memetic algorithms (MAs) and how they can be applied to optimization problems. The paper is open access (thanks are due to the University of Málaga/CBUA), so everyone can read and benefit from it freely.
Memetic algorithms are a powerful class of metaheuristic optimization techniques that combine elements of population-based algorithms (such as genetic algorithms) with local search methods. By incorporating problem-specific search mechanisms, MAs can explore the search space more efficiently and improve solution quality. In this work, a step-by-step approach to designing and implementing MAs for optimization problems is provided, exploring the key components of the algorithm and providing guidance on how to tailor these to fit different optimization problems. A detailed case study on a constrained combinatorial optimization problem, namely aircraft landing scheduling, is included.
Following our project’s commitment to Open Science, both the code used in the experimentation and the data instances considered have been made publicly available. The paper citation details follow:
Cotta, C. Harnessing memetic algorithms: a practical guide. TOP (2025). https://doi.org/10.1007/s11750-024-00694-8