Cher Collègue,
Je me permets de vous adresser ce mail pour vous demander de bien vouloir diffuser à vos étudiants les deux propositions de thèses ci-dessous.
Sujet 1 : Parallel hybrid metaheuristics on GPU
Sujet 2 : Mixing Meta-Modeling and Data-Mining for Explicit Modeling of User Traces, Digital Footprints and Online Reputation
Très cordialement.
Lhassane
================================================
PhD Thesis Proposal: Parallel hybrid metaheuristics on GPU
================================================
• Supervizors: Lhassane IDOUMGHAR and Julien LEPAGNOT
• Contacts: [log in to unmask], [log in to unmask]
• Location: LMIA Laboratory, Université de Haute Alsace
• Expected start time: October 2013
• Duration: 36 months
• Application deadline: June 15th, 2013
• Keywords: metaheuristics, GPU programming optimization, distributed computing
• Funding: French Government Research Grant (approximately 1300€ net per month including French health care coverage).
Context
The design of efficient methods for combinatorial optimization is a key issue for many industrial sectors (automotive, aerospace, broadcasting, etc.). Indeed, more and more efficient heuristics and exact methods have been proposed in recent years, enabling the resolution of many difficult problems.
Metaheuristics, such as evolutionary algorithms, particle swarm optimization and ant colony optimization, have been successfully used to solve many hard problems. It is an interesting approach to tackle high-dimensional problems. In the literature, a large and well-diversified set of metaheuristics has been proposed over the years, enabling the resolution of a wide range of problems.
To take advantage of this diversity, hybrid metaheuristics have been proposed. In this new class of algorithms, a smart combination of different optimization methods is used. Such hybridizations can be used to take advantage of strengths from different algorithms. Several examples in the literature show that hybrid metaheuristics can provide a more robust and efficient problem-solving, especially for real-world and large-scale problems.
However, this kind of hybridizations is mainly achieved statically and the parameter setting is performed experimentally. To overcome this limitation, we first need to define a proper set of hybridization parameters (how to combine two approaches, when to instantiate a particular approach, etc.). We wish through this thesis to answer these questions, which can help in the design of hybrid metaheuristics, and lead in term to advanced adaptive hybrid metaheuristics (using dynamic and adaptive hybridization).
Recently, graphics processing units (GPU) have emerged as a new popular support for massively parallel computing, mainly thanks to the publication of the CUDA development toolkit. The algorithms developed during this thesis will exploit a GPU cluster (a computer cluster in which each node is equipped with a GPU), through the use of GPGPU. This cluster provides the significant computing power required by these algorithms, especially if they are used to solve problems that require high computational capabilities.
Objective
Development of a library of massively parallel hybrid metaheuristics for single-objective/multi-objective optimization.
Work plan
- First step: consists in
• studying the evolution of the search process of hybrid metaheuristics;
• extracting useful information that will help us hybridizing metaheuristics.
- Second step: consists in studying two problems currently treated in our team: the design of an electric motor and the structural resolution of new zeolites. The goal of this study is to determine and propose a classification of the different parameters to be optimized.
- Third step: consists in integrating the knowledge acquired on these problems in order to refine the exploration process used to find better solutions.
Working Conditions
The developed algorithms will be validated using a GPU cluster composed of 12 computers equipped with GTX 680 cards. This cluster is funded by the Scientific Council of University of Haute Alsace.
Prerequisites
• The applicant must hold a Master or equivalent in Computer Science or Applied Mathematics;
• He should have a strong background in GPU programming, metaheuristics and optimization;
• The applicant must speak English fluently ((Knowledge of French is not a prerequisite, work will be conducted in English);
• He should have good programming skills (programming language: C++).
Application
Candidates should submit the following documents:
• Motivation letter;
• Curriculum vitae;
• List of publications (if available);
• Copies of diplomas;
• University transcript.
=====================================================
PhD Thesis Proposal: Mixing Meta-Modeling and Data-Mining for Explicit Modeling of User Traces, Digital Footprints and Online Reputation
=====================================================
• Supervisors:
Pr. Pierre-Alain Muller, Dr. Frédéric Fondement, Dr. Germain Forestier
• Contact: [log in to unmask] (http://pierrealain.muller.free.fr)
• Location: MIPS- LSI Research Group, Université de Haute Alsace
• Expected start time: October 2013
• Duration: 36 months
• Application deadline: June 15th, 2013
• Keywords: Meta-Modeling, Data-Mining
• Funding: French Government Research Grant (approximately 1300€ net per month including French health care coverage).
Scientific Objective
Meta-modeling is a branch of software engineering concerned with analysis and synthesis of models (descriptions of real world phenomena).
The overall goal of data-mining is to extract information from a data sets and transform it into understandable structures for further use.
The scientific objective of this PhD thesis proposal is to investigate how meta-modeling and data-mining might complement themselves.
Application
Field MIPS has been involved in several tracking campaigns within the Internet, and has gathered huge amounts of behavioral data which will serve as experimental ground for this PhD thesis.
“You” was chosen in 2006 as Time magazine “Person of the Year”. Since then, on-line presence of individual never stopped to grow. Data generated from on-line interactions continues to explode: social platform, e-commerce, on-line video game, blogs, etc. The challenge is to turn the raw data collected into actionable insights.
New approaches, tools and methods are now needed to help users to master they on-line identity, and for marketers to benefit from this great amount of information to gain competitive edges.
The application field for this PhD thesis might therefore be online marketing.
Prerequisites
• Applicants must have a degree in Computer Science, or in a related study, with excellent results
• Applicants might have an experience in either meta-modeling or data-mining
• Applicants must be able to demonstrate interest in scientific research
• Applicants must have proven writing skills in English (Knowledge of French is not a prerequisite, work will be conducted in English)
Application Candidates should submit the following documents:
• Motivation letter (including a research statement)
• Curriculum vitae
• List of publications (if available)
• Copies of diplomas, and university transcript
--
Julien LEPAGNOT
Associate professor in computer science / Maître de conférences en informatique
Université de Haute-Alsace - Laboratoire LMIA - Equipe MAGE (EA 3993)
4 rue des Frères Lumière, 68093 Mulhouse
Tel : +33 3 89 33 60 28