Most cited publications

My h-index is 45. The following are my forty-five most cited publications (see Google Scholar for an up to date version of this list)


[J12] M. Dorigo, M. Birattari, and T. Stützle (2006). Ant colony optimization: Artificial ants as a computational intelligence technique. [PDF] IEEE Computational Intelligence Magazine, 1(4):28-39 [11954 citations]

[J33] M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo (2013).   Swarm robotics: A review from the swarm engineering perspective.[PDF]   Swarm Intelligence, 7(1):1-41 [848 citations]

[P18] M. Birattari, T. Stützle, L. Paquete, and K. Varrentrapp (2002). A racing algorithm for configuring metaheuristics. [PDF] In W.B. Langdon, E. Cantú-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M.A. Potter, A.C. Schultz, J.F. Miller, E. Burke, and N. Jonoska (Eds.) GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 11-18. Morgan Kaufmann, San Francisco, CA, USA [595 citations] — Winner of the 2012 SIGEVO Impact Award

[R095] M. López-Ibáñez, J. Dubois-Lacoste, T. Stützle, and M. Birattari (2011).   The irace package — Iterated race for automatic algorithm configuration.  Technical Report TR/IRIDIA/2011-004.   IRIDIA, Université Libre de Bruxelles, Brussels, Belgium [393 citations]

[J55] M. López-Ibáñez, J. Dubois-Lacoste, L. Pérez Cáceres, M. Birattari, and T. Stützle (2016).   The irace package: Iterated racing for automatic algorithm configuration.[PDF]   Operations Research Perspectives, 3:43--58 [381 citations]

[B02] M. Birattari (2009). Tuning Metaheuristics: A machine learning perspective. Springer, Berlin, Germany [338 citations]

[C05] M. Birattari, Z. Yuan, P. Balaprakash, and T. Stützle (2010).   F-race and iterated F-race: An overview.   In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss (Eds.) Empirical Methods for the Analysis of Optimization Algorithms, pp. 311-336, Springer, Berlin, Germany [327 citations]

[J38] M. Dorigo, D. Floreano, L. M. Gambardella, F. Mondada, S. Nolfi, T. Baaboura, M. Birattari, M. Bonani, M. Brambilla, A. Brutschy, D. Burnier, A. Campo, A. L. Christensen, A. Decugnière, G. Di Caro, F. Ducatelle, E. Ferrante, A. Förster, J. Martinez Gonzales, J. Guzzi, V. Longchamp, S. Magnenat, N. Mathews, M. Montes de Oca, R. O'Grady, C. Pinciroli, G. Pini, P. Rètornaz, J. Roberts, V. Sperati, T. Stirling, A. Stranieri, T. Stützle, V. Trianni, E. Tuci, A. E. Turgut, and F. Vaussard (2013).   Swarmanoid: A novel concept for the study of heterogeneous robotic swarms.   IEEE Robotics & Automation Magazine. 20(4)60-71 [326 citations]

[J20] M. A. Montes de Oca, T. Stützle, M. Birattari, and M. Dorigo (2009).   Frankenstein's PSO: A composite particle swarm optimization algorithm. [PDF]   IEEE Transactions on Evolutionary Computation, 13(5):1120-1132 [307 citations]

[J05] M. Zlochin, M. Birattari, N. Meuleau, and M. Dorigo (2004). Model-based search for combinatorial optimization: A critical survey. [PDF] Annals of Operations Research, 131: 373-395 [292 citations]

[J30] C. Pinciroli, V. Trianni, R. O'Grady, G. Pini, A. Brutschy, M. Brambilla, N. Mathews, E. Ferrante, G. Di Caro, F. Ducatelle, M. Birattari, L. M. Gambardella, and M. Dorigo (2012).   ARGoS: a modular, multi-engine simulator for heterogeneous swarm robotics.[PDF]   Swarm Intelligence, 6(4):271-295 [281 citations]

[J01] G. Bontempi, M. Birattari, and H. Bersini (1999). Lazy learning for local modeling and control design. [PDF] International Journal of Control, 72(7/8):643-658 [278 citations]

[C02] O. Rossi-Doria, M. Sampels, M. Birattari, M. Chiarandini, M. Dorigo, L.M. Gambardella, J. Knowles, M. Manfrin, M. Mastrolilli, B. Paechter, L. Paquete, and T. Stützle (2003). A comparison of the performance of different metaheuristics on the timetabling problem. [PDF] In E. Burke and P.D Causmaecker (Eds.) Practice and Theory of Automated Timetabling IV. 4th International Conference, PATAT 2002. LNCS 2740, pp. 329-351. Springer, Berlin, Germany [263 citations]

[P37] P. Balaprakash, M. Birattari, T. Stützle (2007).   Improvement strategies for the F-Race algorithm: Sampling design and iterative refinement. [PDF]   In T. Bartz-Beielstein, M. J. Blesa Aguilera, C. Blum, B. Naujoks, A. Roli, G. Rudolph, and M. Sampels (Eds.) Hybrid Metaheuristics, 4th International Workshop, HM 2007, LNCS 4771, pp. 108-122. Springer. Berlin, Germany [230 citations]

[J15] M. Dorigo and M. Birattari (2007).   Swarm Intelligence. [PDF]   Scholarpedia, 2(9):1462 [212 citations]

[T03] M. Birattari (2004).   The Problem of Tuning Metaheuristics as Seen from a Machine Learning Perspective. [PDF]   PhD Thesis, Université Libre de Bruxelles. [175 citations]

[J10] M. Chiarandini, M. Birattari, K. Socha, and O. Rossi-Doria (2006). An effective hybrid algorithm for university course timetabling. [PDF] Journal of Scheduling, 9(5):403-432 [167 citations]

[J09] L. Bianchi, M. Birattari, M. Chiarandini, M. Manfrin, M. Mastrolilli, L. Paquete, O. Rossi-Doria, and T. Schiavinotto (2006). Hybrid metaheuristics for the vehicle routing problem with stochastic demands. [PDF] Journal of Mathematical Modelling and Algorithms, 5(1):91-110 [163 citations]

[C09] T. Stützle, M. López-Ibañez, P. Pellegrini, M. Maur, M. Montes de Oca, M. Birattari, and M. Dorigo (2012).   Parameter adaptation in ant colony optimization.   In Y. Hamadi, E. Monfroy, and F. Saubion (Eds.) Autonomous Search, pp. 191-215. Springer, Berlin, Germany [162 citations]

[P26] M. Manfrin, M. Birattari, T. Stützle, and M. Dorigo (2006). Parallel ant colony optimization for the traveling salesman problem. [PDF] In M. Dorigo, L. M. Gambardella, M. Birattari, A. Martinoli, R. Poli, and T. Stützle (Eds.) Ant Colony Optimization and Swarm Intelligence, 5th International Workshop, ANTS 2006, LNCS 4150 pp. 224-234. Springer, Berlin, Germany [153 citations]

[P08] M. Birattari, G. Bontempi, and H. Bersini (1999). Lazy learning meets the recursive least squares algorithm. [PDF] In M.S. Kearns, S.A. Solla, and D.A. Cohn (Eds.) NIPS'98: Advances in Neural Information Processing Systems 11. pp. 375-381. MIT Press, Cambridge, MA, USA [133 citations]

[J14] M. Birattari, P. Pellegrini, and M. Dorigo (2007).   On the invariance of ant colony optimization. [PDF]   IEEE Transactions on Evolutionary Computation, 11(6):732-742 [107 citations]

[J03] G. Bontempi, H. Bersini, and M. Birattari (2001). The local paradigm for modeling and control: From neuro-fuzzy to lazy learning. [PDF] Fuzzy Sets and Systems, 121(1):59-72 [105 citations]

[J27] M. Montes de Oca, E. Ferrante, A. Scheidler, C. Pinciroli, M. Birattari, and M. Dorigo (2011).   Majority-rule opinion dynamics with differential latency: A mechanism for self-organized collective decision-making.[PDF]   Swarm Intelligence, 5(3/4):305-327 [103 citations]

[P20] M. Birattari, G. Di Caro, and M. Dorigo (2002). Toward the formal foundation of ant programming. [PDF] In M. Dorigo, G. Di Caro, and M. Samples (Eds.) Ant Algorithms. Third International workshop, ANTS 2002. LNCS 2463, pp. 188-201, Springer, Berlin, Germany [95 citations]

[J16] A. L. Christensen, R. O'Grady, M. Birattari, and M. Dorigo (2008).   Fault detection in autonomous robots based on fault injection and learning. [PDF]   Autonomous Robots, 24(1):49-67 [91 citations]

[J41] M. Dorigo, M. Birattari, M. Brambilla (2014).   Swarm robotics.   Scholarpedia, 9(1):1463 [86 citations]

[J44] 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.[PDF]   Swarm Intelligence, 8(2):89-112 [84 citations]

[P22] L. Bianchi, M. Birattari, M. Chiarandini, M. Manfrin, M. Mastrolilli, L. Paquete, O. Rossi-Doria, and T. Schiavinotto (2004). Metaheuristics for the vehicle routing problem with stochastic demands. [PDF] In X. Yao, E. Burke, J. A. Lozano, J. Smith, J. J. Merelo-Guervós, J. A. Bullinaria, J. Rowe, P. Tino, A. Kabán, and H.-P. Schwefel (Eds.) Parallel Problem Solving from Nature - PPSN VIII, 8th International Conference. LNCS 3242, pp. 450-460. Springer, Berlin, Germany [84 citations]

[J39] A. Brutschy, G. Pini, C. Pinciroli, M. Birattari, and M. Dorigo (2014).   Self-organized task allocation to sequentially interdependent tasks in swarm robotics.[PDF]   Autonomous Agents and Multi-Agent Systems, 28(1):101-125 [76 citations]

[R018] M. Birattari, L. Paquete, T. Stützle, and K. Varrentrapp (2001).   Classification of Metaheuristics and Design of Experiments for the Analysis of Components.   Technical Report AIDA-01-05.   Intellektik, Technische Universität Darmstadt, Darmstadt, Germany [75 citations]

[J22] C. Twomey, T. Stützle, M. Dorigo, M. Manfrin, and M. Birattari (2010).   An analysis of communication policies for homogeneous multi-colony ACO algorithms. [PDF]   Information Sciences, 180(12):2390-2404 [74 citations]

[C06] M. Dorigo and M. Birattari (2010).   Ant Colony Optimization. [PDF]   In C. Sammut and G. Webb Encyclopedia of Machine Learning, pp. 37-40, Springer, Berlin, Germany [67 citations]

[J26] G. Pini, A. Brutschy, M. Frison, A. Roli, M. Dorigo, and M. Birattari (2011).   Task partitioning in swarms of robots: An adaptive method for strategy selection.[PDF]   Swarm Intelligence, 5(3/4):283-304 [62 citations]

[P09] G. Bontempi, M. Birattari, and H. Bersini (1999). Local learning for iterated time-series prediction. [PDF] In I. Bradko and S. Dzeroski (Eds.) ICML'99: International Conference on Machine Learning, pp. 32-38. Morgan Kaufmann, San Francisco, CA, USA [60 citations]

[J21] P. Balaprakash, M. Birattari, T. Stützle, Z. Yuan, and M. Dorigo (2009).   Estimation-based ant colony optimization and local search for the probabilistic traveling salesman problem. [PDF]   Swarm Intelligence, 3(3):223-242 [58 citations]

[J18] M. Birattari, P. Balaprakash, T. Stützle, and M. Dorigo (2008).   Estimation-based local search for stochastic combinatorial optimization using delta evaluations: A case study on the probabilistic traveling salesman problem. [PDF]   INFORMS Journal on Computing, 20(4):644-658 [56 citations]

[J13] M. Birattari and M. Dorigo (2007).   How to assess and report the performance of a stochastic algorithm on a benchmark problem: Mean or best result on a number of runs? [PDF]   Optimization Letters, 1(3):309-311 [55 citations]

[J29] Z. Yuan, M. Montes de Oca, M. Birattari, and T. Stützle (2012).   Continuous optimization algorithms for tuning real and integer parameters of swarm intelligence algorithms.[PDF]   Swarm Intelligence, 6(1):49-75 [54 citations]

[P24] M. Birattari, P. Balaprakash, and M. Dorigo (2005).   ACO/F-Race: Ant colony optimization and racing techniques for combinatorial optimization under uncertainty. [PDF]   In K. F. Doerner, M. Gendreau, P. Greistorfer, W. J. Gutjahr, R. F. Hartl, and M. Reimann (Eds.) MIC 2005: The 6th Metaheuristics International Conference, pp. 107-112 [54 citations]

[P60] M. Brambilla; C. Pinciroli; M. Birattari; and M. Dorigo (2012).   Property-driven design for swarm robotics. [PDF]   In V. Conitzer, M. Winikoff, L. Padgham, and W. van der Hoek (Eds.) Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2012, pp.139-146. IFAAMAS [52 citations]

[P29] A. Campo, S. Nouyan, M. Birattari, R. Groß, and M. Dorigo (2006).   Negotiation of goal direction for cooperative transport. [PDF]   In M. Dorigo, L. M. Gambardella, M. Birattari, A. Martinoli, R. Poli, and T. Stützle (Eds.) Ant Colony Optimization and Swarm Intelligence, 5th International Workshop, ANTS 2006, LNCS 4150 pp. 191-202. Springer, Berlin, Germany [51 citations]

[P02] G. Bontempi, M. Birattari, and H. Bersini (1998).   Recursive lazy learning for modeling and control. [PDF]   In C. Nédellec and C. Rouveirol (Eds.) Machine Learning: ECML-98. 10th European Conference on Machine Learning. LNCS 1398, pp. 292-303. Springer, Berlin, Germany [51 citations]

[P56] A. Roli, M. Manfroni, C. Pinciroli, and M. Birattari (2011).   On the design of Boolean network robots. [PDF]   In C. Di Chio, A. Brabazon, G.A. Di Caro, R. Drechsler, M. Farooq, J. Grahl, G. Greenfield, C. Prins, J. Romero, G. Squillero, E. Tarantino, A.G.B. Tettamanzi, N. Urquhart, and A.S. Uyar (Eds.) Applications of Evolutionary Computation, EvoApplications 2011: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, LNCS 6624/5, pp. 43-52. Springer. Berlin, Germany [48 citations]

[J46] M. Brambilla, A. Brutschy, M. Dorigo, and M. Birattari (2014).   Property-driven design for robot swarms: A design method based on prescriptive modeling and model checking.[PDF]   ACM Transactions on Autonomous and Adaptive Systems, 9(4):17/1-17/28 [46 citations]

Last update: Nov 26, 2019