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teaching:mfe:ia [2015/04/11 17:45] stuetzle [Automatic fine-tuning of an evolutionary multi-objective framework] |
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)]] | ||