Sana Ben Hamida
23
Documents
Presentation
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.
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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⟩
Journal articles
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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⟩
Journal articles
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Recovering Volatility from Option Prices by Evolutionary OptimizationThe Journal of Computational Finance, 2005, ⟨10.2139/ssrn.546882⟩
Journal articles
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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⟩
Conference papers
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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⟩
Conference papers
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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⟩
Conference papers
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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⟩
Conference papers
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Hierarchical Data Topology Based Selection for Large Scale LearningUIC/ATC/ScalCom/CBDCom/IoP/SmartWorld (2016 Intl IEEE Conferences), Jul 2016, Toulouse, France. pp.1221-1226, ⟨10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0186⟩
Conference papers
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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⟩
Conference papers
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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⟩
Conference papers
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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⟩
Conference papers
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An Adaptive Algorithm for constrained optimization problemsPPSN 2000, Sep 2000, Paris, France
Conference papers
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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
Books
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Evolutionary AlgorithmsJohn Wiley & Sons, Ltd, 2017
Books
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Sampling Methods in Genetic Programming Learners from Large Datasets: A Comparative StudyAdvances in Big Data: Proceedings of the 2nd INNS Conference on Big Data, October 23-25, 2016, Thessaloniki, Greece, pp.50-60, 2016, ⟨10.1007/978-3-319-47898-2_6⟩
Book sections
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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⟩
Book sections
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Evolutionary algorithmsMetaheuristics, Springer, pp.115 - 178, 2016, 978-3-319-45401-6. ⟨10.1007/978-3-319-45403-0_6⟩
Book sections
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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
Book sections
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Dynamic Hedging using Generated Genetic Programming Implied Volatility ModelsGenetic Programming - New Approaches and Successful Applications, InTech, 2012, ⟨10.5772/48148⟩
Book sections
hal-02490809v1
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Liage des données par les systèmes de recommandation intelligents dans une démarche d'optimisation de la qualité des données2021
Preprints, Working Papers, ...
hal-03452652v1
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Nested Monte Carlo Expression Discovery vs Genetic Programming for Forecasting Financial Volatility2020
Preprints, Working Papers, ...
hal-02489115v1
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Algorithmes Evolutionnaires: Prise en compte des contraintes et Application RéelleInformatique [cs]. Université Paris Saclay, 2001. Français. ⟨NNT : ⟩
Theses
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