Practitioners using heuristic algorithms for hard optimization problems are confronted with the burden of selecting the most appropriate method, in many cases through expensive algorithm configuration and parameter tuning . Scientists seek theoretical insights and demand a sound experimental methodology for evaluating algorithms and assessing strengths and weaknesses. This effort requires a clear separation between the algorithm and the experimenter, who, in too many cases, is "in the loop" as a motivated intelligent learning component. LION deals with 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 online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components.

This meeting explores the intersections and uncharted territories between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems. Russia has a long tradition in optimization theory, computational mathematics and "intelligent learning techniques" (in particular cybernetics and statistics). The location of LION11 in Nizhny is an occasion to meet researchers and consolidate research and human links.

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