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LIONsolver
| Developer(s) | Reactive Search srl |
|---|---|
| Stable release | 2.0.198 / October 9, 2011 (2011-10-09) |
| Operating system | Windows , Mac OS X, Unix |
| Available in | English |
| Type | Business intelligence software |
| License | Proprietary software, free for academic use |
| Website | lionoso.com |
LIONsolver is an integrated software for data mining, business intelligence, analytics, and modeling
Learning and Intelligent OptimizatioN[1] and reactive business intelligence approach.[2] A non-profit version is available as LIONoso.
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.
Contents
1 Overview
2 See also
3 References
4 External links
Overview
LIONsolver originates from research principles in Reactive Search Optimization[3] 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,[4]
which provides a direct way of defining design spaces involving Reactive Search Optimization, and
of Autonomous Search
[5] 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[6]
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.[7]
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.[8]
See also
- Multi-objective optimization
References
^ Battiti, Roberto; Mauro Brunato (2014). The LION way. Machine Learning plus Intelligent Optimization. Trento, Italy: LIONlab, University of Trento. ISBN 978-14-960340-2-1..mw-parser-output cite.citation{font-style:inherit}.mw-parser-output q{quotes:"""""""'""'"}.mw-parser-output code.cs1-code{color:inherit;background:inherit;border:inherit;padding:inherit}.mw-parser-output .cs1-lock-free a{background:url("//upload.wikimedia.org/wikipedia/commons/thumb/6/65/Lock-green.svg/9px-Lock-green.svg.png")no-repeat;background-position:right .1em center}.mw-parser-output .cs1-lock-limited a,.mw-parser-output .cs1-lock-registration a{background:url("//upload.wikimedia.org/wikipedia/commons/thumb/d/d6/Lock-gray-alt-2.svg/9px-Lock-gray-alt-2.svg.png")no-repeat;background-position:right .1em center}.mw-parser-output .cs1-lock-subscription a{background:url("//upload.wikimedia.org/wikipedia/commons/thumb/a/aa/Lock-red-alt-2.svg/9px-Lock-red-alt-2.svg.png")no-repeat;background-position:right .1em center}.mw-parser-output .cs1-subscription,.mw-parser-output .cs1-registration{color:#555}.mw-parser-output .cs1-subscription span,.mw-parser-output .cs1-registration span{border-bottom:1px dotted;cursor:help}.mw-parser-output .cs1-hidden-error{display:none;font-size:100%}.mw-parser-output .cs1-visible-error{font-size:100%}.mw-parser-output .cs1-subscription,.mw-parser-output .cs1-registration,.mw-parser-output .cs1-format{font-size:95%}.mw-parser-output .cs1-kern-left,.mw-parser-output .cs1-kern-wl-left{padding-left:0.2em}.mw-parser-output .cs1-kern-right,.mw-parser-output .cs1-kern-wl-right{padding-right:0.2em}
^ 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. doi:10.1287/ijoc.6.2.126.
^ Holger, Hoos (2012). "Programming by optimization" (PDF). Communications of the ACM. 55 (2): 70–80. doi:10.1145/2076450.2076469.
^ Youssef, Hamadi; E. Monfroy; 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.
^ ""Machine Learning Approach" to Smartphone Data Garners $10,000 First Prize in The Michael J. Fox Foundation Parkinson's Data Challenge". MJFF. April 24, 2013.
External links
- LIONsolver official non-profit site
