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