Mauro Birattari

Chercheur Qualifié du F.R.S.-FNRS

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Introducing Myself

I am a research associate (chercheur qualifié) of the fund for scientific research F.R.S.-FNRS of Belgium's French Community. I am affiliated with IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium.

My research focuses mainly on computational intelligence and in particular on the automatic design of swarm intelligence systems and multi-robot systems.

My research interests include also metaheuristics and local search methods for combinatorial optimization. I am particularly interested in the application of statistical methods, design of experiments, and machine learning techniques, notably for assessing the performance of algorithms and for fine-tuning their parameters.

I am also interested in epistemology and in the philosophical foundations of computational intelligence.

I am an associate editor for Swarm Intelligence and an academic editor for the journal PeerJ Computer Science.

I served as a member of the organizing committee for 15 international conferences, and as a member of the program committee for more than 50 international conferences in the last five years. Since 2002, I am a member of the organizing committee of the series of conferences ANTS.

What's New?

Swarmanoid, the movie

The video "Swarmanoid, the movie" won the Best Video Award at the AAAI-11 AI Video Competition!

This video, written and directed by Mauro Birattari and Rehan O'Grady, presents the main results of the Swarmanoid project, a FET-OPEN project funded by the European Commission and coordinated by Marco Dorigo. For more information, see the website of the Swarmanoid project.

Media coverage


SCI197 Tuning Metaheuristics

A machine learning perspective
M. Birattari. Springer, 2009

About this book

The importance of tuning metaheuristics is widely acknowledged. However, there is very little dedicated research on the subject. Typically, scientists and practitioners tune metaheuristics by hand, guided only by their experience and by some rules of thumb. Tuning metaheuristics is often considered to be more of an art than a science.

This book lays the foundations for a scientific approach to tuning metaheuristics. The fundamental intuition that underlies Birattari's approach is that the tuning problem has much in common with the problems that are typically faced in machine learning. By adopting a machine learning perspective, the author gives a formal definition of the tuning problem, develops a generic algorithm for tuning metaheuristics, and defines an appropriate experimental methodology for assessing the performance of metaheuristics.

Mauro Birattari

Research Associate, F.R.S.-FNRS

IRIDIA - CP 194/6
Université Libre de Bruxelles
Av. F.D. Roosevelt 50
1050 Bruxelles - Belgium

Voice: +32 2 650 31 68
Fax: +32 2 650 27 15
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New Article

An Experiment in Automatic Design of Robot Swarms: AutoMoDe-Vanilla, EvoStick, and Human Experts. Swarm Intelligence, 9th International Conference, ANTS 2014. LNCS 8667, pp 25-37. 2014.

September 2014

New Article

AutoMoDe: A novel approach to the automatic design of control software for robot swarms. Swarm Intelligence, 8(2):89-112. 2014.

July 2014

SIGEVO Impact Award

The article A racing algorithm for configuring metaheuristics won the 2012 SIGEVO Impact Award.

"Established in 2010, the SIGEVO Impact award recognizes up to three papers a year that were published in the GECCO conference 10 years earlier and which are both highly cited and deemed to be seminal by the SIGEVO Executive Committee."

July 2012

Best Video Award

The video Swarmanoid, the movie, written and directed by Mauro Birattari and Rehan O'Grady, won the Best Video Award at the AAAI-11 AI Video Competition!

August 2011

News from IRIDIA

Mattia Manfroni received the Best Master Thesis Award from the Italian Association for Artificial Intelligence (AI*IA). Mattia carried out his research at IRIDIA under the supervision of Andrea Roli, Mauro Birattari, Carlo Pinciroli, and Marco Dorigo.

September 2011


Tuning Metaheuristics

A machine learning perspective
M. Birattari. Springer, 2009

Quote of the Month

Nothing is particularly hard if you divide it into small jobs

Henry Ford