“LIONsolver”的意思、由来-开放百科全书

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.

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

which provides a direct way of defining design spaces involving Reactive Search Optimization, and

Version 2.0 of the software was released on Oct 1, 2011, covering also the Unix and Mac OS X operating

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.

permits interactive multi-objective optimization, with a user interface for visualizing the results and facilitating

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

On Apr 24, 2013 LIONsolver received the first prize of the Michael J. Fox Foundation –

See also

References

1. ^{{cite book|title=The LION way. Machine Learning plus Intelligent Optimization. |last=Battiti|first=Roberto|authorlink=|author2=Mauro Brunato |year=2014|publisher=LIONlab, University of Trento|location= Trento, Italy|isbn=978-14-960340-2-1|url=http://intelligent-optimization.org/LIONbook/}}
2. ^{{cite book|title=Reactive Search and Intelligent Optimization|last=Battiti|first=Roberto|authorlink=|author2=Mauro Brunato |author3=Franco Mascia |year=2008|publisher=Springer Verlag|location=|isbn=978-0-387-09623-0}}
3. ^{{cite journal| last =Battiti| first =Roberto| authorlink =|author2=Gianpietro Tecchiolli | editor-last =| editor-first=| editor-link =| year =1994| title =The reactive tabu search.|trans-title=| journal =ORSA Journal on Computing| volume =6| issue =2| series =| pages =126–140| publisher =| location =| issn =| pmid =| pmc =| doi =10.1287/ijoc.6.2.126| bibcode =| oclc =| id =| url =http://rtm.science.unitn.it/~battiti/archive/TheReactiveTabuSearch.PDF| language =| format =PDF| accessdate =| laysummary =| laysource =| laydate =| quote =| ref =| postscript =}}
4. ^{{cite journal| last =Holger| first =Hoos| authorlink =| editor-last =| editor-first=| editor-link =| year =2012| title =Programming by optimization.|trans-title=| journal =Communications of the ACM| volume =55| issue =2| series =| pages =70–80| publisher =| location =| issn =| pmid =| pmc =| doi = 10.1145/2076450.2076469| bibcode =| oclc =| id =| url =http://doi.acm.org/10.1145/2076450.2076469| language =| format =PDF| accessdate =| laysummary =| laysource =| laydate =| quote =| ref =| postscript =}}
5. ^{{cite book|title=Autonomous Search|last=Youssef|first=Hamadi|authorlink= |author2=E. Monfroy |author3=F. Saubion|year=2012|publisher=Springer Verlag|location=New York|isbn=978-3-642-21433-2}}
6. ^{{cite journal| last =Battiti| first =Roberto| authorlink =|author2=Mauro Brunato | editor-last =| editor-first=| editor-link =| year =2010| title =Grapheur: A Software Architecture for Reactive and Interactive Optimization .|trans-title=Proceedings Learning and Intelligent OptimizatioN LION 4, Jan 18-22, 2010, Venice, Italy.| journal =Lecture Notes in Computer Science| volume =6073| issue =| series =| pages =232–246| publisher =| location =| issn =| pmid =| pmc =| doi = 10.1007/978-3-642-13800-3| bibcode =| oclc =| id =| url =http://rtm.science.unitn.it/~battiti/archive/grapheur-lion4.pdf| language =| format =PDF| accessdate =| laysummary =| laysource =| laydate =| quote =| ref =| postscript =}}
7. ^{{cite journal| last =Battiti| first =Roberto| authorlink =|author2=Andrea Passerini | editor-last =| editor-first=| editor-link =| year =2010| title =Brain-Computer Evolutionary Multi-Objective Optimization (BC-EMO): a genetic algorithm adapting to the decision maker.|trans-title=| journal =IEEE Transactions on Evolutionary Computation| volume =14| issue =15| series =| pages =671–687| publisher =| location =| issn =| pmid =| pmc =| doi =10.1109/TEVC.2010.2058118 | bibcode =| oclc =| id =| url =http://rtm.science.unitn.it/~battiti/archive/bcemo.pdf| language =| format =PDF| accessdate =| laysummary =| laysource =| laydate =| quote =| ref =| postscript =}}
8. ^{{cite web|title="Machine Learning Approach" to Smartphone Data Garners $10,000 First Prize in The Michael J. Fox Foundation Parkinson's Data Challenge|date=April 24, 2013|url=https://www.michaeljfox.org/foundation/publication-detail.html?id=473&category=7|publisher=MJFF}}

Overview

See also

References

External links