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jeudi 6 novembre 2008

2nd International Conference on Metaheuristics and Nature Inspired Computing

I've just attend the META 2008 international conference on metaheuristics and nature inspired computing.

The weather was nice in Tunisia, we had a place to sleep, a restaurant and a swimming pool, the organization was just fine. The acceptance rate was of 60%, with 116 accepted papers, for 130 attendants and one short review by paper (at least for mine).

OK, now let's talk about what is really exciting: science.

I was more than pleased to attend to two tutorials, given by Dick Thierens and Thomas Stützle, that both were talking about the use of stochastic local search.

What was definitely interesting is that these two talented researchers were insisting a lot on the need of a rigorous experimental approach for the design and the validation of metaheuristics. That's good news for our research domain: the idea that metaheuristics should be employed in a scientific manner rather than in an artistic one gains more and more importance.

First, they both says that a good way to tackle a hard optimization problem is to employ a bottom-up approach: start first with a simple local search, then use metaheuristics operators to improve the results.

Thomas Stützle, particularly, insist on the crucial need of rigorous parameter setting and experimental validation with statistical tests. That's definitely a very important point.

Another good point made by Thomas is the use of the term "algorithm engineering" to describe a rigorous design and evaluation approach of optimization algorithms. I was searching a nice term to name it, I think this one is a good candidate. The bad news at this conference is that, despite these two incredible tutorials, there was very few peoples speaking about algorithm engineering. I was presenting a new method for parameter setting and behaviour understanding, but I was in a unrelated "metaheuristics for real-world problem" session. I haven't seen other works specifically dedicated to such subjects.

More badly, I have attend to several presentations with very bad experimental work. Some peoples keeps telling their stochastic algorithm is better only by showing the best result found. More often, their is a mean and a standard deviation, but without a statistical test. But there is hope as, since 2001 (when some works made by Éric D. Taillard definitely introduced experimental validation for metaheuristics, at least for me), I find that the proportion of better experimental plans is increasing in the literature.

Anyway, my wish is that there will be more and more special sessions on algorithm engineering in future conferences on metaheuristics. In the meantime, there is the 2nd "Engineering Stochastic local search algorithms" conference, in september 2009, in Brussels, that seems really interesting...

mardi 30 septembre 2008

Tools for conferences about metaheuristics

Here is some tools interesting when trying to attend conferences about metaheuristics :

lundi 3 mars 2008

A chronology of metaheuristics

At last, I found some time to translate my chronology of metaheuristics I have made for the french Wikipedia. I put it here for the records, but you will find a more up-to-date version on the english Wikipedia article about Metaheuristics.


Detailled chronology


  1. V. Angel, La rugosité des paysages : une théorie pour la difficulté des problèmes d’optimisation combinatoire relativement aux métaheuristiques, thèse de doctorat de l’université de Paris-Sud, Orsay, 1998.
  2. J. Dreo, J.-P. Aumasson, W. Tfaili, P. Siarry, Adaptive Learning Search, a new tool to help comprehending metaheuristics, to appear in the International Journal on Artificial Intelligence Tools.
  3. El-Ghazali Talbi, A taxonomy of hybrid metaheuristics, Journal of Heuristics, volume 8, no 2, pages 541-564, septembre 2002
  4. (en) exemples de fonctions de tests pour métaheuristiques d’optimisation de problèmes continus.
  5. W. Dullaert, M. Sevaux, K. Sörensen et J. Springael, Applications of metaheuristics, numéro spécial du European Journal of Operational Research, no 179, 2007.
  6. Robbins, H. and Monro, S., A Stochastic Approximation Method, Annals of Mathematical Statistics, vol. 22, pp. 400-407, 1951
  7. Barricelli, Nils Aall, Esempi numerici di processi di evoluzione, Methodos, pp. 45-68, 1954
  8. Rechenberg, I., Cybernetic Solution Path of an Experimental Problem, Royal Aircraft Establishment Library Translation, 1965
  9. Fogel, L., Owens, A.J., Walsh, M.J., Artificial Intelligence through Simulated Evolution, Wiley, 1966
  10. W.K. Hastings. Monte Carlo Sampling Methods Using Markov Chains and Their Applications, Biometrika, volume 57, no 1, pages 97-109, 1970
  11. Holland, John H., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975
  12. Smith, S.F., A Learning System Based on Genetic Adaptive Algorithms, PhD dissertation (University of Pittsburgh), 1980
  13. S. Kirkpatrick, C. D. Gelatt et M. P. Vecchi, Optimization by Simulated Annealing, Science, volume 220, no  4598, pages 671-680, 1983
  14. V. Cerny, A thermodynamical approach to the travelling salesman problem : an efficient simulation algorithm Journal of Optimization Theory and Applications, volume45, pages 41-51, 1985
  15. Fred GLover, Future Paths for Integer Programming and Links to Artificial Intelligence, Comput. & Ops. Res.Vol. 13, No.5, pp. 533-549, 1986
  16. J.D. Farmer, N. Packard and A. Perelson, The immune system, adaptation and machine learning, Physica D, vol. 22, pp. 187--204, 1986
  17. F. Moyson, B. Manderick, The collective behaviour of Ants : an Example of Self-Organization in Massive Parallelism, Actes de AAAI Spring Symposium on Parallel Models of Intelligence, Stanford, Californie, 1988
  18. Koza, John R. Non-Linear Genetic Algorithms for Solving Problems. United States Patent 4,935,877. Filed May 20, 1988. Issued June 19, 1990
  19. Goldberg, David E., Genetic Algorithms in Search, Optimization and Machine Learning, Kluwer Academic Publishers, Boston, MA., 1989
  20. P. Moscato, On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms, Caltech Concurrent Computation Program, C3P Report 826, 1989.
  21. M. Dorigo, Optimization, Learning and Natural Algorithms, Thèse de doctorat, Politecnico di Milano, Italie, 1992.
  22. Feo, T., Resende, M., Greedy randomized adaptive search procedure, Journal of Global Optimization, tome 42, page 32--37, 1992
  23. Eberhart, R. C. et Kennedy, J., A new optimizer using particle swarm theory, Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan. pp. 39-43, 1995
  24. Kennedy, J. et Eberhart, R. C., Particle swarm optimization, Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ. pp. 1942-1948, 1995
  25. Mülhenbein, H., Paaß, G., From recombination of genes to the estimation of distribution I. Binary parameters, Lectures Notes in Computer Science 1411: Parallel Problem Solving from Nature, tome PPSN IV, pages 178--187, 1996
  26. Rainer Storn, Kenneth Price, Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces, Journal of Global Optimization, volume 11, no 4, pages 341-359, 1997
  27. Rubinstein, R.Y., Optimization of Computer simulation Models with Rare Events, European Journal of Operations Research, 99, 89-112, 1997
  28. Stefan Boettcher, Allon G. Percus, "Extremal Optimization : Methods derived from Co-Evolution", Proceedings of the Genetic and Evolutionary Computation Conference (1999)
  29. Takagi, H., Active user intervention in an EC Search, Proceesings of the JCIS 2000

vendredi 1 septembre 2006

Finding references about metaheuristics (and computer science)

The Internet is rapidly becoming the first place to search for scientific papers. But the number of places gathering ressources becomes really high. Here is a list of web sites with free access. These are places where you can find some stuff about metaheuristics, I have not include all the available databases (nor journals web pages), despite the fact that metaheuristics are often apply to a wide range of fields.

  • Online collective bibliography managers are really usefull for picking references when surfing on journal sites, they permits to automatically gather the reference's informations, tag them, share your list with others and export it in your local reference manager.
  • digital libraries or paper databases:
    • CiteSeer, search for citations and papers, show citations inside each papers, permits corrections on items, really interesting for computer science.
    • ScienceDirect, database, require registration, make available a watchlist based on email alerts.
    • arXiv, an e-print service, well formated ressources, RSS syndication.
    • Springer Links, books & papers database, RSS syndication, watchlist for registered users.
    • ACM portal, digital library, no syndication or free watchlist for registered users.
    • IEEE Xplore, database, no syndication, no watchlist, email alerts at field level only.
    • The Collection of Computer Science Bibliographies, database, RSS syndication.
    • PubMed, database, emails alerts for registered users.
    • Backwell Synergy, database, RSS/Atom syndication, email alerts.
    • Optimization Online, e-print about optimization, monthly email alerts.
    • Scitation, database, no syndication, no free email alerts.
    • Wiley InterScience, database, no syndication, emails alerts for registerd users.
  • generalistic search-engines:

mardi 22 août 2006

A new book about "stigmergic optimization"

Springer has published a new book in their Studies in Computational Intelligence series.

Edited by Abraham, Grosan and Ramos, it covers "Stigmergic Optimization". Looking at the table of content, one can see that we talk about Ant Colony Optimization.

Of course, it also get onto the so-called swarm intelligence and self-organization, but also onto Particle Swarm Optimization, which is a method that gather more and more researchers these months.

mercredi 2 août 2006

Introductive bibliography to metaheuristics for hard optimization

Note that this bibliography is quite old (2003) and aim french students.

If you need only one reference, this is (of course, because I'm one of the authors) this one :

  • Dréo, J. ; Petrowski, A. ; Taillard, E. ; Siarry, P. ; Metaheuristics for Hard Optimization Methods and Case Studies, Springer, 2006, XII, 369 p., 140 illus., Hardcover. ISBN: 3-540-23022-X



  • Glover, F. W. ; Kochenberger, G. A. ; 2003 : Handbook of Metaheuristics, Kluwer Academic Publishers, International series in operations research and management science, Boston Hardbound.
  • Teghem, J. ; Pirlot, M. ; 2002 : Optimisation approchée en recherche opérationnelle. Recherches locales, réseaux neuronaux et satisfaction de contraintes, Hermès.
  • Pham, D.T. ; Karaboga, D. ; 2000 : Intelligent optimisation techniques. Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks, Springer.
  • Saït, S.M. ; Youssef, H. ; 1999 : Iterative computer algorithms with applications in engineering, IEEE Computer Society Press.
  • Reeves, C.R., 1995 : Modern Heuristic Techniques for Combinatorial Problems, Mc Graw-Hill, Advances topics in computer science.


Simulated Annealing

  • Siarry, P.; Dreyfus, G. ; 1989 : La méthode du recuit simulé : théorie et applications, ESPCI -- IDSET, 10 rue Vauquelin, 75005 Paris.

Tabu Search

  • Glover, F. ; Laguna, M. ; 1997 : Tabu search, Kluwer Academic Publishers, Dordrecht.
  • Glover, F. ; 1989 : Tabu search --- part I, ORSA Journal on Computing, vol. 1, 190--206.
  • Glover, F. ; 1990 : Tabu search --- part II, ORSA Journal on Computing, vol. 2, 4--32.

Evolutionary Algorithms (aka Genetic Algorithm)

  • Baeck, T. ; Fogel, D. B. ; Michalewicz, Z. ; 2000 : Evolutionary computation 1: basic algorithms and operators, Institute of Physics Publishing.
  • Baeck, T. ; Fogel, D. B. ; Michalewicz, Z. ; 2000 : Evolutionary computation 2: advanced algorithms and operators, Institute of Physics Publishing.
  • Goldberg, D. E. ; 1994 : Algorithmes génétiques. exploration, optimisation et apprentissage automatique, Addison-Wesley France.
  • Koza, J. R. ; 1992 : Genetic programming I: on the programming of computers by means of natural selection, MIT Press.
  • Koza, J. R. ; 1994 : Genetic programming II: automatic discovery of reusable programs, MIT Press.

Ant Colony Algorithms

  • Bonabeau, E. ; Dorigo, M. ; Theraulaz, G. ; 1999 : Swarm Intelligence, From Natural to Artificial Systems, Oxford University Press.

Greedy Randomized Adaptive Search Procedure (GRASP)

Tech Reports
  • Resende, M.G.C. ; 2000 : Greedy randomized adaptive search procedures (GRASP), AT&T Labs-Research, TR 98.41.1.

Partical Swarm Optimization

  • Eberhart, R.C. ; Kennedy, J. ; Shi, Y. ; 2001 : Swarm Intelligence, Morgan Kaufmann, Evolutionnary Computation.

Estimation of Distribution Algorithms

  • Larranaga, P. ; Lozano, J.A. ; 2002 : Estimation of Distribution Algorithms, A New Tool for Evolutionnary Computation, Kluwer Academic Publishers, Genetic Algorithms and Evolutionnary Computation.

Related Topics

Multi-Objective Optimization

  • Collette, Y. ; Siarry, P. ; 2002 : Optimisation multiobjectif, Eyrolles.
  • Deb, K. ; 2001 : Multi-objective optimization using evolutionary algorithms, John Wiley and sons.

Constrainted Optimization

  • Michalewicz, Z. ; 1996 : Genetic algorithms + data structures = evolution programs, Springer Verlag, troisième édition révisée.


  • Camazine, S. ; Deneubourg, J.L. ; Franks, N. ; Sneyd, J. ; Theraulaz, G. ; Bonabeau, E. ; 2000 : Self-Organization in Biological Systems, Princeton University Press.