Publications

Books

Research Reports

Journal papers

Conference papers

  • A. Liefooghe, M. Basseur, L. Jourdan, E.-G. Talbi, "ParadisEO-MOEO: A Framework for Evolutionary Multi-objective Optimization", EMO 2007, LNCS Vol. 4403, pp. 386-400, Matsushima, Japan.
  • S. Cahon, N. Melab and E-G. Talbi, "ParadisEO: a framework for metaheuristics", International Workshop on Optimization Frameworks for Industrial Applications (ICOPI 2005), Paris, France, October 19-21, 2005.
  • S. Cahon, N. Melab and E-G. Talbi, "An Enabling Framework for Parallel Optimization on the Computational Grid", In Proceedings of the fifth IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'2005), Cardiff, UK, May, 2005.
  • S. Cahon, N. Melab, E-G. Talbi and M. Schoenauer, "PARADISEO based design of parallel and distributed evolutionary algorithms", Evolutionary Algorithms EA'2003, Marseille, France, LNCS, October 2003.


Applications

ParadisEO has been experimented on different academic and industrial problems. In this section, we present different applications that show the wide range of potential of this framework as it has been applied to scheduling problems, continuous optimization, data-mining applications, bioinformatic applications, telecommunication problem ...

Telecommunications

Cellular network design is a major issue in mobile telecommunication systems. A model of the problem in its full practical complexity, based on multiobjective constrained combinatorial optimization, has been investigated. We adopted the Pareto approach at resolution in order to compute a set of diversified non-dominated networks, thus removing the need for the designer to rank or weight objectives a priori.

Ref:

  • E-G. Talbi, S. Cahon and N. Melab. "Designing cellular networks using a parallel hybrid metaheuristic". Journal of Computer Communications, Elsevier Science, To appear in 2006.

A bi-objective flow-shop scheduling problem.

The flow-shop is one of the most widely investigated scheduling problem of the literature. But, the majority of studies considers it on a single-criterion form. However, other objectives than minimizing the makespan can be taken into account, like, e.g., minimizing the total tardiness.

Ref:

  • Arnaud Liefooghe, Matthieu Basseur, Laetitia Jourdan, El-Ghazali Talbi. "Combinatorial Optimization of Stochastic Multi-objective Problems: an Application to the Flow-shop Scheduling Problem". In S. Obayashi et al. (Eds.): Evolutionary Multi-Criterion Optimization (EMO 2007), LNCS vol. 4403, pp. 457-471, Matsushima, Japan (2007)

Electromagnetic properties of conducting polymer composites in the microwave band.

Due to the proliferation of electromagnetic interferences, designing protecting material for high frequencies equipments has become an important problem. A new multi-objective model is proposed to design the different layers of a conducting polymer. To solve this model, a multi-objective continuous genetic algorithm is used. This algorithm offers several solutions with different physical properties and different costs.

Ref:

  • Oliver Schuetze, Laetitia Jourdan, Thomas Legrand, El-Ghazali Talbi, Jean Luc Wojkiewicz, "A Multi-Objective Approach to the Design of Conducting Polymer Composites for Electromagnetic Shielding", EMO 2007, LNCS Vol. XX, pp. XX-XX, Matsushima, Japan.

Knowledge discovery in biological data from microarray experiments.

The problem of analyzing microarray data is actually a major issue in genomics. Often used techniques are clustering and classification. The authors propose to analyze those data through association rules. The problem is modeled as a multi-objective rule mining problem and a genetic algorithm is used to explore the large search space associated. Thence, MOGA permitted to present previously undiscovered knowledge.

Ref:

  • M. Khabzaoui, C. Dhaenens and E-G. Talbi, "A Cooperative Genetic Algorithm for Knowledge Discovery in MicroArray Experiments", In Parallel Computing for Bioinformatics and Computational Biology, Edited by Albert Y. Zomaya, ISBN: 0-471-71848-3, Chapter 13, pp 305-326, April 2006.
  • L. Jourdan, M. Khabzaoui, C. Dhaenens and E-G. Talbi, "A hybrid metaheuristic for knowledge discovery in microarray experiments", In Handbook of Bioinspired Algorithms and Applications, Edited by S. Olariu and A.Y. Zomaya, CRC Press, USA, ISBN: 1-58488-475-4, October 2005.

Molecular Docking and Protein Structure Prediction

Ref:

  • Alexandru-Adrian Tantar, Nouredine Melab, El-Ghazali Talbi, "A Comparative Study of Parallel Metaheuristics for Protein Structure Prediction on the Computational Grid", IEEE International Parallel & Distributed Processing Symposium(IPDPS 2007), pp. 1-10, 26-30 March 2007, Long Beach, California, USA.
  • A-A. Tantar, N. Melab, E-G. Talbi, O. Dragos and B. Parent, "A Parallel Hybrid Genetic Algorithm for Protein Structure Prediction on the Computational Grid", In Future Generation Computer Systems, Elsevier Science, vol. 23(3), pp. 398-409, 2007.
  • Alexandru-Adrian Tantar, Nouredine Melab, El-Ghazali Talbi and Bernard Toursel,"Solving the Protein Folding Problem with a Bicriterion Genetic Algorithm on the Grid", Fourth International Workshop on Biomedical Computations on the Grid(BioGrid'06), May 16-19, 2006, Singapore.

Protein identification.

Ref:

  • J-C Boisson, L. Jourdan, E-G. Talbi and C. Rolando "Protein Sequencing with an Adaptive Genetic Algorithm from Tandem Mass Spectrometry", CEC 2006, 0-7803-9489-5, July 16-21 2006, pp 1412-1419, Vancouver, Canada.
  • J-C. Boisson, L. Jourdan, E-G. Talbi and C. Rolando, "A Preliminary Work on Evolutionary Identification of Protein Variants and New Proteins on Grids", Second IEEE Workshop on High Performance Computing in Medicine and Biology (HiPComb 2006). Proccedings of the 20th International Conference on Advance Information Networking and Application, vol. 2, pp. 583-587. Vienne, Austria, April 18-20, 2006.