This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
|
teaching:mfe:ia [2015/03/26 14:38] bersini [Mise en place de solutions Big Data et Elastic Search pour les réseaux bibliométriques] |
teaching:mfe:ia [2016/03/11 11:46] stuetzle [Optimising Ant Colony Algorithms for Performance] |
||
|---|---|---|---|
| Line 1: | Line 1: | ||
| - | ====== MFE 2013-2014 : Intelligence Artificielle ====== | + | ====== MFE 2015-2016 : Intelligence Artificielle ====== |
| ===== Introduction ===== | ===== Introduction ===== | ||
| Line 11: | Line 11: | ||
| Ce mémoire se fera en collaboration avec l'équipe médicale du centre pour l'anorexie et la boulimie de l'hôpital Erasme. Il consistera en l'analyse informatisée des données récoltées lors d'entretiens avec le patient et sa famille au cours du traitement. Les données sont actuellement stockées dans dans une base de données SPSS. Le mémoire consistera pour l'essentiel au traitement de ces données par des approches "Machine Learning" et "Data Mining" dans une perspective de Quality Management. | Ce mémoire se fera en collaboration avec l'équipe médicale du centre pour l'anorexie et la boulimie de l'hôpital Erasme. Il consistera en l'analyse informatisée des données récoltées lors d'entretiens avec le patient et sa famille au cours du traitement. Les données sont actuellement stockées dans dans une base de données SPSS. Le mémoire consistera pour l'essentiel au traitement de ces données par des approches "Machine Learning" et "Data Mining" dans une perspective de Quality Management. | ||
| + | |||
| + | * Contact : [[http://code.ulb.ac.be/iridia.people.php?id=1|Hugues Bersini (IRIDIA)]] | ||
| + | |||
| + | ===== Développer un programme informatique permettant une analyse statistique en vue de l'évaluation d'un module psychothérapeutique. ===== | ||
| + | |||
| + | Ce mémoire se fera en collaboration avec l'équipe médicale du centre pour l'anorexie et la boulimie de l'hôpital Erasme. Il consistera en l'analyse informatisée des données récoltées lors d'entretiens avec le patient et sa famille au cours du traitement. Les données sont actuellement stockées dans dans une base de données SPSS. Le mémoire consistera pour l'essentiel au traitement de ces données par des approches "Machine Learning" et "Data Mining" dans une perspective de Quality Management. | ||
| + | |||
| + | * Contact : [[http://code.ulb.ac.be/iridia.people.php?id=1|Hugues Bersini (IRIDIA)]] | ||
| + | |||
| + | ===== Contribution au développement de la plateforme Big Data d'IRIDIA ===== | ||
| + | |||
| + | Dans de nombreux projets d'enseignement et d'industrie dans lesquels IRIDIA se trouvent impliqués, des demandes pressantes nous sont faites de formation et d'utilisation des outils Big Data (Map-Reduce, Cloudera, Hue, Hive, Pig, Elastic Search, HBase). Nous répondons tant bien que mal à ces demandes et tentons d'équiper notre cluster avec les logiciels demandés. Le mémoire consisterait en une étude comparative de l'existant en terme surtout logiciel et la mise en place d'un cours mettant en évidence les avantages de l'une ou l'autre solution en ce compris par rapport à des solutions plus conventionnels de type BD relationnelle. | ||
| * Contact : [[http://code.ulb.ac.be/iridia.people.php?id=1|Hugues Bersini (IRIDIA)]] | * Contact : [[http://code.ulb.ac.be/iridia.people.php?id=1|Hugues Bersini (IRIDIA)]] | ||
| Line 165: | Line 177: | ||
| * [[http://iridia.ulb.ac.be/~mdorigo|Marco Dorigo (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~mdorigo|Marco Dorigo (IRIDIA)]] | ||
| * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~manuel|Manuel López-Ibáñez (IRIDIA)]] | ||
| - | |||
| - | ===== Optimising Ant Colony Algorithms for Performance ====== | ||
| - | |||
| - | Ants have inspired a number of computational techniques and among the most successful is ant colony optimization (ACO). ACO is an optimization technique that can be applied to tackle a wide variety of computational problems that arise in computer science, telecommunications, and engineering. | ||
| - | |||
| - | The goal of this project is to improve the performance of ACO algorithms by investigating and testing various implementation techniques: intrinsic functions (MMX/SSE floating-point operations), CPU cache effects, or GPU programming. | ||
| - | |||
| - | Required skills: knowledge of C programming. Some knowledge about computer architecture. | ||
| - | |||
| - | * Contacts : | ||
| - | * [[http://iridia.ulb.ac.be/~mdorigo|Marco Dorigo (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~manuel|Manuel López-Ibáñez (IRIDIA)]] | ||
| - | |||
| - | |||
| - | |||
| - | ===== Graphical Tools for analysing Multi-Objective Data. ===== | ||
| - | |||
| - | In multi-objective problems, not only one objective function must be minimised but several, often conflicting, objectives must be taken into account. The result is often a set of solutions modelling the trade-off between the objectives of the problem. | ||
| - | |||
| - | The goal of this project is to develop graphical tools to interactively examine and compare the results of algorithms for multi-objective problems. | ||
| - | |||
| - | Required skills: The candidate should be well acquainted with programming in object oriented languages. | ||
| - | |||
| - | |||
| - | * Contacts : | ||
| - | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~manuel|Manuel López-Ibáñez (IRIDIA)]] | ||
| Line 217: | Line 198: | ||
| * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
| * [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~mbiro|Mauro Birattari (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~manuel|Manuel López-Ibáñez (IRIDIA)]] | + | * [[http://iridia.ulb.ac.be/~lperez|Leslie Perez (IRIDIA)]] |
| - | * [[http://iridia.ulb.ac.be/~jdubois|Jérémie Dubois-Lacoste (IRIDIA)]] | + | |
| - | |||
| - | ===== Stochastic Local Search heuristics for solving NP-complete puzzles. ====== | ||
| - | |||
| - | This project is about single player games (puzzles) and the design of algorithms for tackling hard combinatorial optimisation problems. | ||
| - | Example puzzles are: [[http://en.wikipedia.org/wiki/Light_Up|Light Up]], [[http://en.wikipedia.org/wiki/Mastermind_(board_game)|Mastermind]], [[http://en.wikipedia.org/wiki/Minesweeper_(video_game)|Minesweeper]], etc. | ||
| - | |||
| - | The student will learn how to design and implement a Stochastic Local Search algorithm to solve NP-complete puzzles. The student will also learn how to analyse the performaces of the algorithm and perform statistically sound comparisons with the other algorithms available in literature. | ||
| - | |||
| - | Required skills: good knowledge of C or C++ programming. | ||
| - | |||
| - | |||
| - | * Contacts : | ||
| - | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~fmascia|Franco Mascia (IRIDIA)]] | ||
| - | |||
| - | |||
| - | ===== Analysis of Local Optima Networks for the Max-Clique problem. ====== | ||
| - | |||
| - | Stochastic Local Search algorithms search for optimal solutions in a large space of candidate solutions. Local Optima Networks (LON) are representations of search landscapes of combinatorial optimisation problems. In these networks, nodes are local optima of the problem, and edges are weighted transitions between the optima. These networks model in a more compact way the properties of the larger search spaces they represent. | ||
| - | |||
| - | The goal of this project is to build LON of small instances of the Maximum Clique (MC) problem, and measure properties that could illustrate the differences between instance families. | ||
| - | |||
| - | The MC problem is an NP-hard combinatorial optimisation problem that asks to find the biggest completely connected component of a graph. It has relevant applications in information retrieval, computer vision, social network analysis, computational biochemistry, bioinformatics and genomics. | ||
| - | |||
| - | |||
| - | Required skills: good knowledge of C or C++ programming. | ||
| - | |||
| - | |||
| - | * Contacts : | ||
| - | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~fmascia|Franco Mascia (IRIDIA)]] | ||
| Line 267: | Line 216: | ||
| * Contacts : | * Contacts : | ||
| * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~fmascia|Franco Mascia (IRIDIA)]] | + | * [[http://code.ulb.ac.be/iridia.people.php?id=1393|Alberto Franzin (IRIDIA)]] |
| Line 548: | Line 497: | ||
| - | ===== Applications of the Multi-objective ACO framework ===== | + | ===== Applications of a hybrid SLS algorithm framework ===== |
| - | We have recently developed a software framework of Ant Colony Optimization algorithms for multi-objective optimization problems. This framework has only been applied to a few problems. The goal of this project would be to extend this framework to other problems and compare its results with the methods proposed in the literature. The student will learn to solve multi-objective optimization problems with ACO algorithms, automatic configuration of optimization algorithms, and analysis and comparison of optimization algorithms for multi-objective problems. | + | We have recently developed a software framework from which hybrid stochastic local search algorithms can be designed automatically. This framework has only been applied to a few problems. The goal of this project would be to extend this framework to other problems and compare its results with the methods proposed in the literature. The student will learn to solve combinatorial optimization problems with SLS algorithms, automatic configuration of optimization algorithms, and analysis and comparison of optimization algorithms. |
| * Contacts : | * Contacts : | ||
| - | * [[http://iridia.ulb.ac.be/~manuel|Manuel López-Ibáñez (IRIDIA)]] | ||
| * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~mdorigo|Marco Dorigo (IRIDIA)]] | + | * [[http://code.ulb.ac.be/iridia.people.php?id=1388|Federico Pagnozzi (IRIDIA)]] |
| - | ===== A graphical interface for the optimisation of Water Distribution Networks ===== | ||
| - | The [[http://iridia.ulb.ac.be/~manuel/doc/cec2005-presentation.pdf|optimization of the operations of Water Distribution Networks]] may save important amounts of energy and its associated costs, and, therefore, it is an important problem in practice. There are [[http://www.epa.gov/nrmrl/wswrd/dw/epanet.html|graphical tools and simulators]] available. In addition, several optimization methods based on [[http://iridia.ulb.ac.be/~manuel/doc/cec2005.pdf|evolutionary algorithms]] and [[http://dx.doi.org/10.1061/(ASCE)0733-9496(2008)134:4(337)|ant colony optimization]] have been proposed in the literature. The goal of this project is to integrate the optimization algorithms into a graphical environment that can be used by water engineers and operators. No knowledge about water distribution networks is necessary. The optimisation algorithms and toolkit libraries for handling water distribution networks will be available to the student. | ||
| - | |||
| - | * Contacts : | ||
| - | * [[http://iridia.ulb.ac.be/~manuel|Manuel López-Ibáñez (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
| - | |||
| - | |||
| - | ===== Automatic fine-tuning of an evolutionary multi-objective framework ===== | ||
| - | |||
| - | The goal of this project is to explore the possibilities of using automatic configuration tools for fine-tuning an existing [[http://paradiseo.gforge.inria.fr/index.php?n=Paradiseo.MOEO|evolutionary multi-objective framework]]. The student will learn about automatic configuration tools, evolutionary algorithms for multi-objective optimization problems and analysis and comparison of multi-objective algorithms. | ||
| - | |||
| - | * Contacts : | ||
| - | * [[http://iridia.ulb.ac.be/~manuel|Manuel López-Ibáñez (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
| - | |||
| - | |||