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ERCIM Working Group

Computing & Statistics

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Optimization Heuristics in Estimation and Modelling


Estimation and modelling problems as they arise in many fields often turn out to be intractable by standard numerical methods. One way to deal with such a situation consists in simplifying models and procedures. However, the solutions to these simplified problems might not be satisfying. A different approach consists in applying optimization heuristics such as trajectory methods (e.g., simulated annealing, threshold accepting, tabu search), population based methods (e.g., genetic algorithms, differential evolution, ant colonies) or hybrid methods (e.g., memetic algorithms), which have been developed over the last two decades. Although the use of these methods increases in estimation and modelling, there is scope for further applications and a more detailed analysis of the statistical properties of these algorithms.


This track concerns on one hand the computational aspects of estimation and modelling problems, in particular for highly complex cases, when standard algorithms fail. It will also cover real applications in cross-disciplinary fields. On the other hand, the track contributes to the development and evaluation of optimization heuristics in modelling and estimation applications. In particular, it will strive for establishing standards for the tuning of algorithms and for reporting solutions including all necessary information on algorithmic implementation. Furthermore, the statistics of the results obtained by means of optimization algorithms will be analyzed.


Co-Chairs:

Manfred Gilli, University of Geneva, Switzerland. E-mail: Send
Sandra Paterlini, University of Modena and Reggio E., Italy. E-mail: Send
Dietmar Maringer, University of Basel, Switzerland. E-mail: Send
Peter Winker, University of Giessen, Germany. E-mail: Send

Members

    1. Eduardo Acosta-Gonzalez, University of Las Palmas de Gran Canaria, Spain.
    2. Roberto Baragona, University La Sapienza, Rome, Italy.
    3. Francesco Battaglia, University La Sapienza, Rome, Italy.
    4. Jorge Cadima, Instituto Superior de Agronomia, Lisboa, Portugal.
    5. Jorge Orestes Cerdeira, Instituto Superior de Agronomia, Lisboa, Portugal.
    6. Marco Chiarandini, University of Southern Denmark, Denmark.
    7. Gunter Dueck, IBM Deutschland, Mannheim, Germany.
    8. Manfred Gilli, University of Geneva, Switzerland.
    9. Gottfried Haber, University of Klagenfurt, Austria.
    10. Uwe Jaekel, University of Applied Sciences Koblenz, Germany.
    11. George Kapetanios, Queen Mary, University of London, UK.
    12. Thiemo Krink, Allianz Investment Management, Minneapolis, USA.
    13. Dietmar Maringer, University of Basel, Switzerland.
    14. David Moreno, Universidad Carlos III, Spain.
    15. Luis Paquete, Universidade do Algarve, Faro, Portugal.
    16. Sandra Paterlini, University of Modena, Italy.
    17. Irene Poli, University Ca' Foscari of Venice, Italy.
    18. Anne Ruiz-Gazen, University Toulouse 1, France.
    19. Detlef Seese, University of Karlsruhe, Germany.
    20. Katja Specht, University of Applied Sciences, Pforzheim, Germany.
    21. Anna Staszewska-Bistrova, University of Lodz, Poland.
    22. Carlo Vercellis, Politecnico di Milano, Italy.
    23. Peter Winker, University of Giessen, Germany.

Created by Computing & Statistics Working Group 2007