Semantic Graph Mining for Black-Box Optimisation

When:
25/04/2022 – 26/04/2022 all-day
2022-04-25T02:00:00+02:00
2022-04-26T02:00:00+02:00

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LIP6, Sorbonne Université
Durée : 3 ans
Contact : marie-jeanne.lesot@lip6.fr
Date limite de publication : 2022-04-25

Contexte :
The general aim of the thesis is to exploit expert knowledge regarding properties of optimisation algorithms and problems, represented in the formal frameworks of ontologies and conceptual graphs, and to develop tools to extract automatically underlying correlations: the objective is to allow understanding the reasons why an algorithm is more appropriate than others to solve a problem depending on its characterisation and possibly to offer new tools to configure optimisation algorithms.

Sujet :
The thesis work will explore new methods for analysing conceptual graphs and in particular design dedicated frequent pattern mining algorithms: the aim is to identify subgraphs that occur frequently and can thus be interpreted as relevant regularities, exploiting the particular characteristics of conceptual graphs so as to improve both their efficiency and the relevance of the extracted patterns.
The developed approaches will be used for the exploitation of the OPTImisation algorithm benchmarking ONtology OPTION, with the general goal to derive recommendations for algorithm selection.
The thesis is expected to contribute at the cross-roads of the domains of knowledge representation, pattern mining and black-box optimisation.

Profil du candidat :
A Master’s degree in a quantitative field such as Computer Science, Engineering, Statistics, Operations Research, Mathematics is required. We expect willingness to conduct empirical research as well as experience with the python programming language. Since the student will be working in an international research team, they must be proficient in written and spoken English. Knowledge of French is not required. International students are very welcome to apply.

Formation et compétences requises :
Master’s degree in a quantitative field such as Computer Science, Engineering, Statistics, Operations Research, Mathematics
Python programming language

Adresse d’emploi :
LIP6, UMR7606
Sorbonne Université
4 place Jussieu
75005 Paris

Document attaché : 202204080914_2022thesisLIP6GraphMiningForBlackBoxOptimisation.pdf