LIONsolver explained

LIONsolver
Developer:Reactive Search srl
Latest Release Version:2.0.198
Operating System:Windows, Mac OS X, Unix
Language:English
Genre:Business intelligence software
License:Proprietary software, free for academic use

LIONsolver is an integrated software for data mining, business intelligence, analytics, and modeling and reactive business intelligence approach.[1] A non-profit version is also available as LIONoso.

LIONsolver is used to build models, visualize them, and improve business and engineering processes.

It is a tool for decision making based on data and quantitative model and it can be connected to most databases and external programs.

The software is fully integrated with the Grapheur business intelligence and intended for more advanced users.

Overview

LIONsolver originates from research principles in Reactive Search Optimization[2] advocating the use of self-tuning schemes acting while a softwaresystem is running. Learning and Intelligent OptimizatioN refers to the integration of online machine learning schemes into the optimization software, so thatit 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, andof 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 operatingsystems in addition to Windows.

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 facilitatingthe solution analysis and decision making process.The architecture allows for problem-specific extensions, and it isapplicable as a post-processing tool for all optimization schemes with a number ofdifferent potential solutions. When the architecture is tightly coupled to a specificproblem-solving or optimization method, effective interactive schemes where thefinal decision maker is in the loop can be developed.[6]

On Apr 24, 2013 LIONsolver received the first prize of the Michael J. Fox FoundationKaggle Parkinson's Data Challenge, a contest leveraging "the wisdom of the crowd" to benefit people with Parkinson's disease.[7]

See also

External links

Notes and References

  1. Book: Battiti , Roberto . Reactive Search and Intelligent Optimization. Mauro Brunato . Franco Mascia . 2008. Springer Verlag. 978-0-387-09623-0.
  2. Battiti. Roberto. Gianpietro Tecchiolli. 1994. The reactive tabu search.. ORSA Journal on Computing. 6. 2. 126–140. 10.1287/ijoc.6.2.126.
  3. Holger. Hoos. 2012. Programming by optimization.. Communications of the ACM. 55. 2. 70–80. 10.1145/2076450.2076469. free.
  4. Book: Youssef , Hamadi . Autonomous Search. E. Monfroy . F. Saubion. 2012. Springer Verlag. New York. 978-3-642-21433-2.
  5. Book: Battiti , Roberto . Mauro Brunato. Learning and Intelligent Optimization. Lecture Notes in Computer Science. 2010. Proceedings Learning and Intelligent OptimizatioN LION 4, Jan 18-22, 2010, Venice, Italy.. 6073. 232–246. 10.1007/978-3-642-13800-3. 978-3-642-13799-0.
  6. Battiti. Roberto. Andrea Passerini . 2010. Brain-Computer Evolutionary Multi-Objective Optimization (BC-EMO): a genetic algorithm adapting to the decision maker.. IEEE Transactions on Evolutionary Computation. 14. 15. 671–687. 10.1109/TEVC.2010.2058118 .
  7. Web site: "Machine Learning Approach" to Smartphone Data Garners $10,000 First Prize in The Michael J. Fox Foundation Parkinson's Data Challenge. April 24, 2013. MJFF.