LION: Learning and Intelligent Optimization

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Past:   Proceedings on DBLP

Future:  Post a CFP for 2022 or later   |   Invite the Organizers Email

 
 

All CFPs on WikiCFP

Event When Where Deadline
LION 2021 Learning and Intelligent Optimization
Jun 20, 2021 - Jun 25, 2021 Athens, Greece Feb 15, 2021
LION 2020 Learning and Intelligent Optimization
May 24, 2020 - May 28, 2020 Athens Dec 31, 2019
LION 2019 Learning and Intelligent Optimization
May 27, 2019 - May 31, 2019 Chania, Greece Jan 13, 2019
LION 2018 Learning and Intelligent Optimization
Jun 10, 2018 - Jun 15, 2018 Kalamata, Greece Jan 15, 2018
LION 2013 Learning and Intelligent Optimization Conference
Jan 7, 2013 - Jan 11, 2013 Catania, Italy Oct 14, 2012
LION 2011 Learning and Intelligent Optimization
Jan 17, 2011 - Jan 21, 2011 Rome, Italy Oct 16, 2010
LION 2009 Learning And Intelligent Optimization
Jan 14, 2009 - Jan 18, 2009 Trento, Italy Oct 15, 2008
 
 

Present CFP : 2021

Call for Papers
Paper Format
Please prepare your paper in English using the Lecture Notes in Computer Science (LNCS) template, which is available here. Papers must be submitted in PDF.
Types of Submissions
When submitting a paper to LION15, authors are required to select one of the following three types of papers:

Long paper: original novel and unpublished work (max. 15 pages in LNCS format);
Short paper: an extended abstract of novel work (max. 6 pages in LNCS format);
Work for oral presentation only (no page restriction; any format). For example, work already published elsewhere, which is relevant and which may solicit fruitful discussion at the conference.

Topics of Interest
LION15 welcomes high quality submissions on the broad topics of machine learning, artificial intelligence, energy, mathematical programming, optimization (models, algorithms and applications) and heuristics. Additionally, more specialized, submission topics for long and short papers may include, but are not limited to:

automatic solver configuration
parallel methods for search and optimization
hard combinatorial optimization problems
intelligent optimization in health, e-health, bioinformatics and neurosciences
machine learning and optimization methods in tourism and hospitality
nature-inspired algorithCall for Papers
Paper Format
Please prepare your paper in English using the Lecture Notes in Computer Science (LNCS) template, which is available here. Papers must be submitted in PDF.
Types of Submissions
When submitting a paper to LION15, authors are required to select one of the following three types of papers:

Long paper: original novel and unpublished work (max. 15 pages in LNCS format);
Short paper: an extended abstract of novel work (max. 6 pages in LNCS format);
Work for oral presentation only (no page restriction; any format). For example, work already published elsewhere, which is relevant and which may solicit fruitful discussion at the conference.

Topics of Interest
LION15 welcomes high quality submissions on the broad topics of machine learning, artificial intelligence, energy, mathematical programming, optimization (models, algorithms and applications) and heuristics. Additionally, more specialized, submission topics for long and short papers may include, but are not limited to:

automatic solver configuration
parallel methods for search and optimization
hard combinatorial optimization problems
intelligent optimization in health, e-health, bioinformatics and neurosciences
machine learning and optimization methods in tourism and hospitality
nature-inspired algorithms for combinatorial optimization problems
hard combinatorial optimization problems in combinatorics, computational geometry and machine learning
DC (difference of convex functions) learning: theory, algorithms and applications
computational intelligence for smart cities
computational intelligence for autonomous driving
optimization and management in smart manufacturing
algorithms and applied optimization for environmental data science
machine learning and robust optimization techniques in finance applications
machine learning and optimization methods in software engineering
ms for combinatorial optimization problems
hard combinatorial optimization problems in combinatorics, computational geometry and machine learning
DC (difference of convex functions) learning: theory, algorithms and applications
computational intelligence for smart cities
computational intelligence for autonomous driving
optimization and management in smart manufacturing
algorithms and applied optimization for environmental data science
machine learning and robust optimization techniques in finance applications
machine learning and optimization methods in software engineering
 

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