LION: Learning and Intelligent Optimization

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Past:   Proceedings on DBLP

Future:  Post a CFP for 2020 or later   |   Invite the Organizers Email

 
 

All CFPs on WikiCFP

Event When Where Deadline
LION 2019 Learning and Intelligent Optimization
May 27, 2019 - May 31, 2019 Chania, Greece Jan 13, 2019
LION 2018 Learning and Intelligent Optimization
Jun 10, 2018 - Jun 15, 2018 Kalamata, Greece Jan 15, 2018
LION 2013 Learning and Intelligent Optimization Conference
Jan 7, 2013 - Jan 11, 2013 Catania, Italy Oct 14, 2012
LION 2011 Learning and Intelligent Optimization
Jan 17, 2011 - Jan 21, 2011 Rome, Italy Oct 16, 2010
LION 2009 Learning And Intelligent Optimization
Jan 14, 2009 - Jan 18, 2009 Trento, Italy Oct 15, 2008
 
 

Present CFP : 2019

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.

This meeting, which continues the successful series of LION events, is aimed at exploring the intersections 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.
 

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