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teaching:mfe:ia [2015/04/11 17:39] stuetzle [Feature Extraction and Automatic Algorithm Selection.] |
teaching:mfe:ia [2016/03/11 11:46] stuetzle [Optimising Ant Colony Algorithms for Performance] |
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| - | ====== MFE 2013-2014 : Intelligence Artificielle ====== | + | ====== MFE 2015-2016 : Intelligence Artificielle ====== |
| ===== Introduction ===== | ===== Introduction ===== | ||
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| * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
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| - | ===== Optimising Ant Colony Algorithms for Performance ====== | ||
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| - | 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. | ||
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| - | 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. | ||
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| - | Required skills: knowledge of C programming. Some knowledge about computer architecture. | ||
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| - | * Contacts : | ||
| - | * [[http://iridia.ulb.ac.be/~mdorigo|Marco Dorigo (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
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| * [[http://iridia.ulb.ac.be/~lperez|Leslie Perez (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~lperez|Leslie Perez (IRIDIA)]] | ||
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| - | ===== Stochastic Local Search heuristics for solving NP-complete puzzles. ====== | ||
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| - | 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. | ||
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| - | 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. | ||
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| - | Required skills: good knowledge of C or C++ programming. | ||
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| - | * Contacts : | ||
| - | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~fmascia|Franco Mascia (IRIDIA)]] | ||
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| * Contacts : | * Contacts : | ||
| * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
| + | * [[http://code.ulb.ac.be/iridia.people.php?id=1393|Alberto Franzin (IRIDIA)]] | ||
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| - | ===== 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)]] |
| - | ===== Automatic fine-tuning of an evolutionary multi-objective framework ===== | ||
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| - | 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. | ||
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| - | * Contacts : | ||
| - | * [[http://iridia.ulb.ac.be/~manuel|Manuel López-Ibáñez (IRIDIA)]] | ||
| - | * [[http://iridia.ulb.ac.be/~stuetzle|Thomas Stützle (IRIDIA)]] | ||
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