Sana Ben Hamida
21
Documents
Présentation
Sana Ben Hamida is an associate professor at Paris Nanterre University and an associate researcher at the computer science laboratory (LAMSADE) of Paris Dauphine University. Her main research topics are evolutionary algorithms, machine learning and related applications. Much of her work focuses on problems related to scaling evolutionary learning techniques for massive data. Sana Ben Hamida is also interested in the application of evolutionary algorithms to solve supervised and unsupervised learning problems in the fields of biology and biodiversity.
Publications
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Genetic Algorithm for Community Detection in Biological NetworksProcedia Computer Science, 2018, 126 (6), pp.195-204. ⟨10.1016/j.procs.2018.07.233⟩
Article dans une revue
hal-02286078v1
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Scale Genetic Programming for large Data Sets: Case of Higgs Bosons ClassificationProcedia Computer Science, 2018, 126, pp.302-311. ⟨10.1016/j.procs.2018.07.264⟩
Article dans une revue
hal-02286084v1
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Recovering Volatility from Option Prices by Evolutionary OptimizationThe Journal of Computational Finance, 2005, ⟨10.2139/ssrn.546882⟩
Article dans une revue
hal-02490586v1
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Genetic Algorithm to Detect Different Sizes’ Communities from Protein-Protein Interaction Networks14th International Conference on Software Technologies, Jul 2019, Prague, Czech Republic. pp.359-370, ⟨10.5220/0007836703590370⟩
Communication dans un congrès
hal-02286178v1
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Genetic Programming over Spark for Higgs Boson Classification22nd International Conference Business Information Systems, Jun 2019, Seville, Spain. pp.300-312, ⟨10.1007/978-3-030-20485-3_23⟩
Communication dans un congrès
hal-02286136v1
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Tuning Active Sampling Techniques for Evolutionary Learner from Big Data Sets: Review and DiscussionUIC/ATC/ScalCom/CBDCom/IoP/SmartWorld (2016 Intl IEEE Conferences), Jul 2016, Toulouse, France. pp.1206-1213, ⟨10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0184⟩
Communication dans un congrès
hal-01448255v1
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Hierarchical Data Topology Based Selection for Large Scale Learning2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), Jul 2016, Toulouse, France. pp.1221-1226, ⟨10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0186⟩
Communication dans un congrès
hal-02286148v1
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Forecasting Financial Volatility Using Nested Monte Carlo Expression Discovery2015 IEEE Symposium Series on Computational Intelligence (SSCI), Dec 2015, Cape Town, France. pp.726-733, ⟨10.1109/SSCI.2015.110⟩
Communication dans un congrès
hal-02476560v1
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Optimal Quantization : Evolutionary Algorithm vs Stochastic Gradient9th Joint Conference on Information Sciences, Oct 2006, Amsterdam, Netherlands. ⟨10.2991/jcis.2006.161⟩
Communication dans un congrès
hal-02490713v1
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The need for improving the exploration operators for constrained optimization problems2000 Congress on Evolutionary Computation, Jul 2000, La Jolla, United States. pp.1176-1183, ⟨10.1109/CEC.2000.870781⟩
Communication dans un congrès
hal-02490606v1
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An Adaptive Algorithm for constrained optimization problemsPPSN 2000, Sep 2000, Paris, France
Communication dans un congrès
inria-00001273v1
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Evolutionary AlgorithmsJohn Wiley & Sons, Ltd, 2017
Ouvrages
hal-02091413v1
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Evolutionary algorithmsISTE Ltd, John Wiley & sons Volume 9, pp.236, 2017, Computer engineering series, metaheuristics set, Computer engineering series, metaheuristics set, 978-1-84821-804-8
Ouvrages
hal-01677858v1
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Evolutionary algorithmsMetaheuristics, Springer, pp.115 - 178, 2016, 978-3-319-45401-6. ⟨10.1007/978-3-319-45403-0_6⟩
Chapitre d'ouvrage
hal-01680390v1
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Sampling Methods in Genetic Programming Learners from Large Datasets: A Comparative StudySpringer. Advances in Big Data, 529, pp.50-60, 2016, Advances in Intelligent Systems and Computing, 978-3-319-47897-5. ⟨10.1007/978-3-319-47898-2_6⟩
Chapitre d'ouvrage
hal-02286097v1
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Les algorithmes évolutionnairesMétaheuristiques : recuit simulé, recherche avec tabous, recherche à voisinages variables, méthode GRASP, algorithmes évolutionnaires, fourmis artificielles, essaims particulaires et autres méthodes d'optimisation, Eyrolles, pp.115 - 173, 2014, Algorithmes, 978-2-212-13929-7
Chapitre d'ouvrage
hal-01263350v1
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Dynamic Hedging using Generated Genetic Programming Implied Volatility ModelsGenetic Programming - New Approaches and Successful Applications, InTech, 2012, ⟨10.5772/48148⟩
Chapitre d'ouvrage
hal-02490809v1
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L'impact du choix des données d'apprentissage dans la génération des IDS par la programmation génétique2008
Autre publication scientifique
hal-02490909v1
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Liage des données par les systèmes de recommandation intelligents dans une démarche d'optimisation de la qualité des données2021
Pré-publication, Document de travail
hal-03452652v1
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Nested Monte Carlo Expression Discovery vs Genetic Programming for Forecasting Financial Volatility2020
Pré-publication, Document de travail
hal-02489115v1
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Algorithmes Evolutionnaires: Prise en compte des contraintes et Application RéelleInformatique [cs]. Université Paris Saclay, 2001. Français. ⟨NNT : ⟩
Thèse
tel-03086421v1
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