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Métaheuristiques pour l'extraction de connaissances: Application à la génomique

Abstract : The work presented in this thesis deals with knowledge discovery with evolutionary algorithms and its application to real cases on genetic data. Firstly, we give a state of art of metaheuristics for knowledge discovery and in particular the use of genetic algorithms. We are particular interested in three fondamental aspects of metaheuristics: the representation of a solution, the fitness function and the choice of operators. Then, we introduce two cases of study that come from a collaboration with the Institute of Biology of Lille about the search for predisposition genetic factors for some multifactorial diseases (diabetes, obesity). We propose to model these problems as knowledge discovery tasks. Then, we modele the identified knowledge discovery tasks as optimization problems and we propose genetic algorithm scheme which has several advanced diversification and intensification mechanisms tasks. These mechanisms are tested separately to evaluate their improvements. We also integrate some knowledge of the biological domain in order to solve the problems under study. This integration is made while designing fitness functions and proposed mechanisms. Finally, some parallelism models are used.
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Contributor : Laetitia Jourdan <>
Submitted on : Monday, January 10, 2005 - 12:12:46 PM
Last modification on : Thursday, February 21, 2019 - 10:52:45 AM
Long-term archiving on: : Friday, April 2, 2010 - 9:36:45 PM


  • HAL Id : tel-00007983, version 1



Laetitia Jourdan. Métaheuristiques pour l'extraction de connaissances: Application à la génomique. Autre [cs.OH]. Université des Sciences et Technologie de Lille - Lille I, 2003. Français. ⟨tel-00007983⟩



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