LION 5, Learning and Intelligent OptimizatioN,
Rome - Italy, Jan 17-21 2010
Short call for papers:
The LION conference aims at exploring the intersections 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 all these areas to present and discuss new ideas, new methods, general trends, challenges and opportunities in applications as well as in research aiming at algorithmic advances. The conference program will consist of plenary presentations, introductory and advanced tutorials, technical presentations, and it will give ample time for discussions.
Scope of the conference
The large variety of heuristic algorithms for hard optimization problems raises numerous interesting and challenging issues. Practitioners are confronted with the burden of selecting the most appropriate method, in many cases through an expensive algorithm configuration and parameter tuning process, and subject to a steep learning curve. Scientists seek theoretical insights and demand a sound experimental methodology for evaluating algorithms and assessing strengths and weaknesses. A necessary prerequisite for this effort is a clear separation between the algorithm and the experimenter, who, in too many cases, is "in the loop" as a crucial intelligent learning component. Both issues are related to designing and engineering ways of "learning" about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained from different runs or during a single run can improve the algorithm development and design process and simplify the applications of high-performance optimization methods. Combinations of algorithms can further improve the robustness and performance of the individual components provided that sufficient knowledge of the relationship between problem instance characteristics and algorithm performance is obtained.
Important Dates
| Submission deadline | October 16, 2010 |
|---|---|
| Notification of acceptance | November 27, 2010 |
| Conference | January 17-21, 2011 |
| Camera ready for post-proceedings | February 18, 2011 |
Relevant Research Areas
LION 5 solicits contributions dealing with all aspects of learning and intelligent optimization. Topics of interest include, but are not limited to:
- Metaheuristics such as tabu search, iterated local search, evolutionary algorithms, memetic algorithms, ant colony optimization, and particle swarm optimization
- Hybridizations of metaheuristics with other techniques for optimization
- Supervised, unsupervised and reinforcement learning applied to heuristic search
- Reactive search optimization
- Self-adaptive algorithms
- Hyperheuristics
- Algorithm portfolios and off-line tuning methods
- Multiscale and multilevel 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
- Software engineering of learning and intelligent optimization methods.
