LIONsolver

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Software product

title: "LIONsolver" type: doc version: 1 created: 2026-02-28 author: "Wikipedia contributors" status: active scope: public tags: ["time-series-software", "data-analysis-software", "data-and-information-visualization-software", "mathematical-optimization-software", "numerical-software"] description: "Software product" topic_path: "science/mathematics" source: "https://en.wikipedia.org/wiki/LIONsolver" license: "CC BY-SA 4.0" wikipedia_page_id: 0 wikipedia_revision_id: 0

::summary Software product ::

::data[format=table title="Infobox software"]

Field	Value
name	LIONsolver
developer	Reactive Search srl
latest release version	2.0.198
latest release date
operating system	Windows, Mac OS X, Unix
language	English
genre	Business intelligence software
license	Proprietary software, free for academic use
website
::

LIONsolver is an integrated software for data mining, business intelligence, analytics, and modeling and reactive business intelligence approach.{{cite book |title=Reactive Search and Intelligent Optimization |last=Battiti |first=Roberto |author2=Mauro Brunato |author3=Franco Mascia |year=2008 |publisher=Springer Verlag |isbn=978-0-387-09623-0 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{{cite journal | last =Battiti | first =Roberto |author2=Gianpietro Tecchiolli | year =1994 | title =The reactive tabu search. | journal =ORSA Journal on Computing | volume =6 | issue =2 | pages =126–140 | doi =10.1287/ijoc.6.2.126 | url =http://rtm.science.unitn.it/~battiti/archive/TheReactiveTabuSearch.PDF 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,{{cite journal | last =Holger | first =Hoos | year =2012 | title =Programming by optimization. | journal =Communications of the ACM | volume =55 | issue =2 | pages =70–80 | doi = 10.1145/2076450.2076469 | doi-access=free which provides a direct way of defining design spaces involving Reactive Search Optimization, and of Autonomous Search |title=Autonomous Search |last=Youssef |first=Hamadi |author2=E. Monfroy |author3=F. Saubion |year=2012 |publisher=Springer Verlag |location=New York |isbn=978-3-642-21433-2 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.

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{{cite book | last =Battiti | first =Roberto |author2=Mauro Brunato | title =Learning and Intelligent Optimization | series =Lecture Notes in Computer Science | year =2010 |trans-title=Proceedings Learning and Intelligent OptimizatioN LION 4, Jan 18-22, 2010, Venice, Italy. | volume =6073 | pages =232–246 | doi = 10.1007/978-3-642-13800-3 | url =http://rtm.science.unitn.it/~battiti/archive/grapheur-lion4.pdf | isbn =978-3-642-13799-0 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. | last =Battiti | first =Roberto |author2=Andrea Passerini | year =2010 | title =Brain-Computer Evolutionary Multi-Objective Optimization (BC-EMO): a genetic algorithm adapting to the decision maker. | journal =IEEE Transactions on Evolutionary Computation | volume =14 | issue =15 | pages =671–687 | doi =10.1109/TEVC.2010.2058118 | url =http://rtm.science.unitn.it/~battiti/archive/bcemo.pdf

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

References

(April 24, 2013). ""Machine Learning Approach" to Smartphone Data Garners $10,000 First Prize in The Michael J. Fox Foundation Parkinson's Data Challenge". MJFF.

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