A few selected publications

[J69] K. Hasselmann, A. Ligot, J. Ruddick, and M. Birattari (2021).   Empirical assessment and comparison of neuro-evolutionary methods for the automatic off-line design of robot swarms.   Nature Communications, 12:4345

[J62] M. Birattari, A. Ligot, and K. Hasselmann (2020).   Disentangling automatic and semi-automatic approaches to the optimization-based design of control software for robot swarms.   Nature Machine Intelligence, 2(9):494–499

[J56] L. Garattoni and M. Birattari (2018).   Autonomous task sequencing in a robot swarm.   Science Robotics, 3(20):eaat0430

[J74] A. Ligot and M. Birattari (2022).   On using simulation to predict the performance of robot swarms.   Scientific Data, 9:788

[J52] G. Francesca, M. Brambilla, A. Brutschy, L. Garattoni, R. Miletitch, G. Podevijn, A. Reina, T. Soleymani, M. Salvaro, C. Pinciroli, F. Mascia, V. Trianni, and M. Birattari (2015).   AutoMoDe-Chocolate: automatic design of control software for robot swarms.   Swarm Intelligence, 9(2/3):125-152

[J45] G. Francesca, M. Brambilla, A. Brutschy, V. Trianni, and M. Birattari (2014).   AutoMoDe: A novel approach to the automatic design of control software for robot swarms.   Swarm Intelligence, 8(2):89-112

[J33] M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo (2013).   Swarm robotics: A review from the swarm engineering perspective.   Swarm Intelligence, 7(1):1-41

[B02] M. Birattari (2009).   Tuning Metaheuristics: A machine learning perspective.   Springer, Berlin, Germany

[J14] M. Birattari, P. Pellegrini, and M. Dorigo (2007).   On the invariance of ant colony optimization.   IEEE Transactions on Evolutionary Computation, 11(6):732-742

[P18] M. Birattari, T. Stützle, L. Paquete, and K. Varrentrapp (2002).   A Racing Algorithm for Configuring Metaheuristics.   In W. B. Langdon et al. (Eds.) GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 11-18. Morgan Kaufmann, San Francisco, CA, USA