Tag Archives: January 2009

Learning and Intelligent OptimizatioN Conference

    Learning and Intelligent OptimizatioN Conference

                              LION 3
                                  

                14-18 January, 2009. Trento, Italy

              More details and up-to-date information at
                www.intelligent-optimization.org/LION3

                                  
Building on the success of the previous editions we are organizing a new event for January 2009. The LION conference is aimed at exploring the boundaries and uncharted territories between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems. The main purpose of the event is to bring together experts from these areas to discuss new ideas and methods, challenges and opportunities in various application areas, general trends and specific developments.

The conference program will consist of plenary presentations, introductory and advanced tutorials, technical presentations, and it will give ample time for discussions.

Relevant Research Areas
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LION 3 solicits contributions dealing with all aspects of learning and intelligent optimization. Topics of interest include, but are not limited to:

– Stochastic local search methods and meta-heuristics
– Hybridizations of constraint and mathematical programming with
meta-heuristics
– Supervised, unsupervised and reinforcement learning applied to
heuristic search
– Reactive search (online self-tuning methods)
– Algorithm portfolios and off-line tuning methods
– Algorithms for dynamic, stochastic and multi-objective problems
– Interface(s) between discrete and continuous optimization
– Experimental analysis and modeling of algorithms
– Theoretical foundations
– Parallelization of optimization algorithms
– Memory-based optimization
– Prohibition-based methods (tabu search)
– Memetic algorithms
– Evolutionary algorithms
– Dynamic local search
– Iterated local search
– Variable neighborhood search
– Swarm intelligence methods (ant colony optimization, particle swarm
optimization etc.)
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