| Automatic off-line design of robot swarms: a manifesto M Birattari, A Ligot, D Bozhinoski, M Brambilla, G Francesca, L Garattoni, ... Frontiers in Robotics and AI 6, 59, 2019 | 80 | 2019 |
| Behavior trees as a control architecture in the automatic modular design of robot swarms J Kuckling, A Ligot, D Bozhinoski, M Birattari International conference on swarm intelligence, 30-43, 2018 | 56 | 2018 |
| Simulation-only experiments to mimic the effects of the reality gap in the automatic design of robot swarms A Ligot, M Birattari Swarm Intelligence 14 (1), 1-24, 2020 | 53 | 2020 |
| Disentangling automatic and semi-automatic approaches to the optimization-based design of control software for robot swarms M Birattari, A Ligot, K Hasselmann Nature Machine Intelligence 2 (9), 494-499, 2020 | 44 | 2020 |
| Empirical assessment and comparison of neuro-evolutionary methods for the automatic off-line design of robot swarms K Hasselmann, A Ligot, J Ruddick, M Birattari Nature communications 12 (1), 4345, 2021 | 42 | 2021 |
| Reference models for AutoMoDe K Hasselmann, A Ligot, G Francesca, M Birattari IRIDIA, Université libre de Bruxelles, Brussels, Belgium, Tech. Rep. TR …, 2018 | 37 | 2018 |
| AutoMoDe: a modular approach to the automatic off-line design and fine-tuning of control software for robot swarms M Birattari, A Ligot, G Francesca Automated design of machine learning and search algorithms, 73-90, 2021 | 27 | 2021 |
| On mimicking the effects of the reality gap with simulation-only experiments A Ligot, M Birattari Swarm Intelligence: 11th International Conference, ANTS 2018, Rome, Italy …, 2018 | 24 | 2018 |
| Concurrent design of control software and configuration of hardware for robot swarms under economic constraints M Salman, A Ligot, M Birattari PeerJ Computer Science 5, e221, 2019 | 21 | 2019 |
| Automatic modular design of robot swarms using behavior trees as a control architecture A Ligot, J Kuckling, D Bozhinoski, M Birattari PeerJ Computer Science 6, e314, 2020 | 19 | 2020 |
| AutoMoDe-arlequin: neural networks as behavioral modules for the automatic design of probabilistic finite-state machines A Ligot, K Hasselmann, M Birattari International Conference on Swarm Intelligence, 271-281, 2020 | 18 | 2020 |
| Toward an empirical practice in offline fully automatic design of robot swarms A Ligot, A Cotorruelo, E Garone, M Birattari IEEE transactions on evolutionary computation 26 (6), 1236-1245, 2022 | 13 | 2022 |
| Complexity measures: open questions and novel opportunities in the automatic design and analysis of robot swarms A Roli, A Ligot, M Birattari Frontiers in Robotics and AI 6, 130, 2019 | 10 | 2019 |
| AutoMoDe, NEAT, and EvoStick: implementations for the e-puck robot in ARGoS3 A Ligot, K Hasselmann, B Delhaisse, L Garattoni, G Francesca, M Birattari IRIDIA, Institut de Recherches Interdisciplinaires et de Développements en …, 2018 | 8 | 2018 |
| On using simulation to predict the performance of robot swarms A Ligot, M Birattari Scientific Data 9 (1), 788, 2022 | 7 | 2022 |
| Search space for AutoMoDe-Chocolate and AutoMoDe-Maple J Kuckling, A Ligot, D Bozhinoski, M Birattari Technical report TR/IRIDIA/2018-012, IRIDIA, Université Libre de Bruxelles …, 2018 | 6 | 2018 |
| Automatic modular design of robot swarms based on repertoires of behaviors generated via novelty search K Hasselmann, A Ligot, M Birattari Swarm and Evolutionary Computation 83, 101395, 2023 | 4 | 2023 |
| The automatic off-line design of robot swarms: recent advances and perspectives DG Ramos, D Bozhinoski, G Francesca, L Garattoni, K Hasselmann, ... R2T2: Robotics Research for Tomorrow’s Technology, 2021 | 4 | 2021 |
| Simulation-only experiments to mimic the effects of the reality gap in the automatic design of robot swarms. Swarm Intell. 14 (1), 1–24 (2019) A Ligot, M Birattari | 4 | |
| Assessing and forecasting the performance of optimization-based design methods for robot swarms: Experimental protocol and pseudo-reality predictors A Ligot Université libre de Bruxelles, 2023 | 3 | 2023 |
