LIONsolver is an integrated software for data mining, business intelligence, analytics, and modeling Learning and Intelligent OptimizatioN and reactive business intelligence approach.[1]
LIONsolver can be used to build models, visualize them, and improve business and engineering processes. It is a tool for decision making based on data and quantitative models, it can be connected to most databases and external programs, it is fully integrated with the Grapheur business intelligence software and intended for more advanced users, interested in designing business logic and processes and not only in simple analytics and visualization tasks.
Overview
LIONsolver originates from research principles in Reactive Search Optimization[2] advocating the use of self-tuning schemes acting while a software system is running. Learning and Intelligent OptimizatioN refers to the integration of online machine learning schemes into the optimization software, so that it becomes capable of learning from its previous runs and from human feedback. A related approach is that of Programming by Optimization,[3] which provides a direct way of defining design spaces involving Reactive Search Optimization, and of Autonomous Search [4] advocating adapting problem-solving algorithms.
Version 2.0 of the software was released on Oct 1, 2011, covering also the Unix and Mac OS X operating systems in addition to Windows. With version 2.0, the modeling and optimization tools, previously contained also in Grapheur, became exclusive of LIONsolver 2.0. The modeling components include neural networks, polynomials, locally-weighted Bayesian regression, k-means clustering, and self-organizing maps. A free academic license for non-commercial use and class use is available.
The software architecture of LIONsolver[5] permits interactive multi-objective optimization, with a user interface for visualizing the results and facilitating the solution analysis and decision making process. The architecture allows for problem-specific extensions, and it is applicable as a post-processing tool for all optimization schemes with a number of different potential solutions. When the architecture is tightly coupled to a specific problem-solving or optimization method, effective interactive schemes where the final decision maker is in the loop can be developed.[6]
See also
References
- ^ Battiti, Roberto; Mauro Brunato; Franco Mascia (2008). Reactive Search and Intelligent Optimization. Springer Verlag. ISBN 978-0-387-09623-0.
- ^ Battiti, Roberto; Gianpietro Tecchiolli (1994). "The reactive tabu search." (PDF). ORSA Journal on Computing 6 (2): 126–140
- ^ Holger, Hoos (2012). "Programming by optimization." (PDF). Communications of the ACM 55 (2): 70–80. doi:10.1145/2076450.2076469
- ^ Youssef, Hamadi; E. Monfroy and F. Saubion (2012). Autonomous Search. New York: Springer Verlag. ISBN 978-3-642-21433-2.
- ^ Battiti, Roberto; Mauro Brunato (2010). "Grapheur: A Software Architecture for Reactive and Interactive Optimization ." [Proceedings Learning and Intelligent OptimizatioN LION 4, Jan 18-22, 2010, Venice, Italy.] (PDF). Lecture Notes in Computer Science 6073: 232–246. doi:10.1007/978-3-642-13800-3
- ^ Battiti, Roberto; Andrea Passerini (2010). "Brain-Computer Evolutionary Multi-Objective Optimization (BC-EMO): a genetic algorithm adapting to the decision maker." (PDF). IEEE Transactions on Evolutionary Computation 14 (15): 671–687. doi:10.1109/TEVC.2010.2058118
External links
