Development of New Bio-inspired Optimisation Approaches forKnowledge Discovery in Biological Data
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The developments of technologies in digital technologies have led in recent years, extremely large volumes of data, which may conceal useful information for organizations that produced them. This data can be in different form and from heterogeneous sources such as biological data. This constant has spawned a field of exploration: the extraction of knowledge from data, also known as KDD (Knowledge discovery for Databases). In this thesis, we developed a new approaches drawing on natural phenomena to solve hard problems based on biological data. We developed a new metaheuristics algorithm based on the penguins behaviors named PeSOA penguins search optimisation algorithm. We applied these new developed algorithms to a set of hard problems of Bioinformatics; biological sequences matching; biological data compression and DNA fragments Assembly
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| Cote | Localisation | Type de Support | Type de Prêt | Statut | Date de Restitution Prévue | Réservation |
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| 004 GHE TH 4 | BIB-Centrale / Thèses | interne | disponible |