Publié le 15 juin 2023 Mis à jour le 15 juin 2023
Complément date
LIMOS - Salle A111

The genetic algorithm (GA) is the most widely used meta-heuristic and nature-inspired algorithm, it is well-known for its efficiency in combinatorial optimization problems. This work focuses on a critical part of GA: population seeding techniques. We aim to answer the question of how to create effective population seeding techniques for GA and what criteria evolving operators (crossover, mutation, and selection) must meet to work in combination with high-quality seeding techniques. First, we investigate the impact of population initialization and each evolving operator on GA. Then we define the characteristics of an initial competitive population. Population seeding techniques can be improved by focusing on these characteristics. We demonstrate our approach by creating new algorithms to solve the TSP (traveling salesman problem) and the MTSP (multi-traveling salesman problem). Experiments on TSPLIB benchmarks show that our algorithms significantly outperform other methods.

Dr. Ta Anh Son got his engineering degree in Mathematics and Informatics (2005) at Hanoi University of Science and Technology, master degree in Numerical Mathematics (2008) at Hanoi University of Science and PhD in Applied Mathemtics (2012) at LMI, INSA de Rouen, France. His research interests include Nonconvex, Combinatorial, D.C. Optimization, and Heuristic Methods, Data Mining, Machine Learning.