LION 2016 - 10th Conference on Learning and Intelligent OptimisatioN (LION)

The large variety of heuristic algorithms for learning and 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
Paper Submission Due: December 18, 2015 23:59 GMT+1;
Notification: March 25, 2016 23:59 GMT+1;
Paper submission
Please prepare your paper in English using the Lecture Notes in Computer Science (LNCS) template. Papers must be submitted in PDF. When submitting a paper to LION10, authors are required to select one of the following three types of papers:
? Long paper: original novel and unpublished work (max. 15 pages in LNCS format);
? Short paper: an extended abstract of novel work (max. 6 pages in LNCS format);
? Work for oral presentation only (no page restriction; any format). For example, work already published elsewhere, which is relevant and which may solicit fruitful discussion at the conference.