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Titre : Artificial Rabbit Optimization For Tuning Deep Learning Parameters Type de document : texte imprimé Auteurs : Yasmine Tigha ; Ibtihel Boussahel ; Semcheddine,Moussa, Directeur de thèse Editeur : Setif:UFA Année de publication : 2023 Importance : 1 vol (84 f .) Format : 29 cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Optimization Artificial Rabbit Optimization Genetic Algorithms Particle
Swarm Optimization Grey Wolf Optimization Deep Learning learning rate and dropout rateIndex. décimale : 004 - Informatique Résumé : Deep learning models’ performance heavily relies on selecting appropriate hyperparameters,
such as learning rate, dropout rate , and network architecture.
However, finding the optimal values for these hyperparameters is challenging due
to the lack of fixed rules.
In this thesis, we propose using bio-inspired algorithms, including ARO, PSO,
GA, and GWO, to optimize the hyperparameters of a feed-forward neural network.
The experiments are conducted on the MNIST dataset, commonly used for image
analysis. Comparing the accuracy of models trained with these algorithms, with
and without dropout, reveals that bio-inspired algorithms improve deep learning
model accuracy. Incorporating bio-inspired algorithms in hyperparameter tuning
shows promise for optimizing deep learning models. By drawing inspiration from
nature, these algorithms enhance performance and generalization, demonstrating
their efficacy in deep learning hyperparameter tuning
Côte titre : MAI/0761 En ligne : https://drive.google.com/file/d/1WcfNow4t2G8wXoZTx2oNMTp_XM-CbHG0/view?usp=drive [...] Format de la ressource électronique : Artificial Rabbit Optimization For Tuning Deep Learning Parameters [texte imprimé] / Yasmine Tigha ; Ibtihel Boussahel ; Semcheddine,Moussa, Directeur de thèse . - [S.l.] : Setif:UFA, 2023 . - 1 vol (84 f .) ; 29 cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Optimization Artificial Rabbit Optimization Genetic Algorithms Particle
Swarm Optimization Grey Wolf Optimization Deep Learning learning rate and dropout rateIndex. décimale : 004 - Informatique Résumé : Deep learning models’ performance heavily relies on selecting appropriate hyperparameters,
such as learning rate, dropout rate , and network architecture.
However, finding the optimal values for these hyperparameters is challenging due
to the lack of fixed rules.
In this thesis, we propose using bio-inspired algorithms, including ARO, PSO,
GA, and GWO, to optimize the hyperparameters of a feed-forward neural network.
The experiments are conducted on the MNIST dataset, commonly used for image
analysis. Comparing the accuracy of models trained with these algorithms, with
and without dropout, reveals that bio-inspired algorithms improve deep learning
model accuracy. Incorporating bio-inspired algorithms in hyperparameter tuning
shows promise for optimizing deep learning models. By drawing inspiration from
nature, these algorithms enhance performance and generalization, demonstrating
their efficacy in deep learning hyperparameter tuning
Côte titre : MAI/0761 En ligne : https://drive.google.com/file/d/1WcfNow4t2G8wXoZTx2oNMTp_XM-CbHG0/view?usp=drive [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0761 MAI/0761 Mémoire Bibliothéque des sciences Anglais Disponible
DisponibleDesign and implementation of software platform for bioinspired optimization algorithms / Islam Bouabdallah
Titre : Design and implementation of software platform for bioinspired optimization algorithms Type de document : texte imprimé Auteurs : Islam Bouabdallah ; Hichem Badis ; Moussa Semchedine, Directeur de thèse Editeur : Setif:UFA Année de publication : 2023 Importance : 1 vol. (56 f.) Format : 29 cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : Optimization Bio-inspired algorithms benchmark functions Particle swarm optimization Firefly
algorithm Grey wolf optimization Whale optmization Artificial rabbit optimizationIndex. décimale : 004 Informatique Résumé : Bio-inspired algorithms have been widely used by scientists because of their ability to solve complex
problems efficiently. In this work, we present the design and implementation of a software platform for bioinspired
optimization algorithms.The platform provides a user-friendly interface for selecting and comparing
different algorithms, including genetic algorithms, particle swarm optimization, and more optimization algorithms.
We also provide a set of benchmark functions to test the performance of the implemented algorithms.The
platform is open source and available for researchers and practitioners in the field of optimization to facilitate
their utilization of these algorithms.Côte titre : MAI/0753 En ligne : https://drive.google.com/file/d/1v5kW5pct42yrVQFRBm8Dkzht5p2rGTjS/view?usp=drive [...] Format de la ressource électronique : Design and implementation of software platform for bioinspired optimization algorithms [texte imprimé] / Islam Bouabdallah ; Hichem Badis ; Moussa Semchedine, Directeur de thèse . - [S.l.] : Setif:UFA, 2023 . - 1 vol. (56 f.) ; 29 cm.
Langues : Français (fre)
Catégories : Thèses & Mémoires:Informatique Mots-clés : Optimization Bio-inspired algorithms benchmark functions Particle swarm optimization Firefly
algorithm Grey wolf optimization Whale optmization Artificial rabbit optimizationIndex. décimale : 004 Informatique Résumé : Bio-inspired algorithms have been widely used by scientists because of their ability to solve complex
problems efficiently. In this work, we present the design and implementation of a software platform for bioinspired
optimization algorithms.The platform provides a user-friendly interface for selecting and comparing
different algorithms, including genetic algorithms, particle swarm optimization, and more optimization algorithms.
We also provide a set of benchmark functions to test the performance of the implemented algorithms.The
platform is open source and available for researchers and practitioners in the field of optimization to facilitate
their utilization of these algorithms.Côte titre : MAI/0753 En ligne : https://drive.google.com/file/d/1v5kW5pct42yrVQFRBm8Dkzht5p2rGTjS/view?usp=drive [...] Format de la ressource électronique : Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité MAI/0753 MAI/0753 Mémoire Bibliothéque des sciences Anglais Disponible
Disponible