The LION events started 20 years ago and explore the intersections and uncharted territories between machine learning, artificial intelligence, operations research and metaheuristics. The conference is run by the strictly non-profit and volunteer-based LION Association. The LION Manifesto defines the research area that is relevant for this event. The venue brings together experts from these areas to discuss new ideas and methods, challenges and opportunities, general trends and specific developments.

The large variety of heuristic and metaheuristic 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.


Topics of Interest:

Learning and Intelligent Optimization
Operations research
Artificial Intelligence
Neural Networks
Machine learning
OR for ML and AI
ML and AI for OR
Metaheuristics
Deep learning, genAI, LLMs
Evolutionary algorithms
Reinforcement learning
Optimization techniques
Data mining and analytics
Data science and big data
Parallel methods for Optimization, OR, ML and AI
Large-scale problems
Robust optimization and its applications
Reactive search optimization (online dynamic self-tuning)
Applications of these topics in robotics, economics, energy, environmental sciences, healthcare, management, and other real-world areas.


When submitting a paper to LION 20, authors are required to select one of the following three types of papers:
Long paper: original novel and unpublished work (12- 15 pages in LNCS format);
Short paper: an extended abstract of novel work (6-11 pages in LNCS format);
Abstract: for oral presentation only (maximum 1000 words in LNCS format).

You can submit original and unpublished work either as a long paper (12-15 pages, including references) or short paper (6-11 pages, including references). You can choose to add an appendix. Please prepare your paper in English using the Springer Lecture Notes in Computer Science (LNCS) template.